mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-10-06 08:29:17 +08:00
add and update samples with CUDA 11.3 support
This commit is contained in:
Rutwik Choughule
parent
067cb65523
commit
568b39bd5b
67
README.md
67
README.md
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@ -1,11 +1,17 @@
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# CUDA Samples
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Samples for CUDA Developers which demonstrates features in CUDA Toolkit. This version supports [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads).
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Samples for CUDA Developers which demonstrates features in CUDA Toolkit. This version supports [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads).
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## Release Notes
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This section describes the release notes for the CUDA Samples on GitHub only.
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### CUDA 11.3
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* Added `streamOrderedAllocationIPC`. Demonstrates Inter Process Communication using one process per GPU for computation.
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* Added `simpleCUBLAS_LU`. Demonstrates batched matrix LU decomposition using cuBLAS API `cublas<t>getrfBatched()`
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* Updated `simpleVulkan`. Demonstrates use of timeline semaphore.
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* Updated multiple samples to use pinned memory using `cudaMallocHost()`.
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### CUDA 11.2
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* Added `streamOrderedAllocation`. Demonstrates stream ordered memory allocation on a GPU using cudaMallocAsync and cudaMemPool family of APIs.
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* Added `streamOrderedAllocationP2P`. Demonstrates peer-to-peer access of stream ordered memory allocated using cudaMallocAsync and cudaMemPool family of APIs.
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@ -103,7 +109,7 @@ This is the first release of CUDA Samples on GitHub:
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### Prerequisites
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Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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For system requirements and installation instructions of cuda toolkit, please refer to the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/), and the [Windows Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html).
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### Getting the CUDA Samples
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@ -160,38 +166,39 @@ The samples makefiles can take advantage of certain options:
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### Samples by OS
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#### Linux
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**[warpAggregatedAtomicsCG](./Samples/warpAggregatedAtomicsCG)** | **[boxFilterNPP](./Samples/boxFilterNPP)** | **[streamOrderedAllocationP2P](./Samples/streamOrderedAllocationP2P)** | **[binaryPartitionCG](./Samples/binaryPartitionCG)** |
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**[bandwidthTest](./Samples/bandwidthTest)** | **[batchedLabelMarkersAndLabelCompressionNPP](./Samples/batchedLabelMarkersAndLabelCompressionNPP)** | **[bf16TensorCoreGemm](./Samples/bf16TensorCoreGemm)** | **[binaryPartitionCG](./Samples/binaryPartitionCG)** |
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---|---|---|---|
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**[dmmaTensorCoreGemm](./Samples/dmmaTensorCoreGemm)** | **[EGLStream_CUDA_Interop](./Samples/EGLStream_CUDA_Interop)** | **[conjugateGradientMultiBlockCG](./Samples/conjugateGradientMultiBlockCG)** | **[simpleIPC](./Samples/simpleIPC)** |
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**[memMapIPCDrv](./Samples/memMapIPCDrv)** | **[vectorAddMMAP](./Samples/vectorAddMMAP)** | **[shfl_scan](./Samples/shfl_scan)** | **[simpleZeroCopy](./Samples/simpleZeroCopy)** |
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**[conjugateGradientCudaGraphs](./Samples/conjugateGradientCudaGraphs)** | **[globalToShmemAsyncCopy](./Samples/globalToShmemAsyncCopy)** | **[cudaNvSciNvMedia](./Samples/cudaNvSciNvMedia)** | **[nvJPEG](./Samples/nvJPEG)** |
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**[batchedLabelMarkersAndLabelCompressionNPP](./Samples/batchedLabelMarkersAndLabelCompressionNPP)** | **[watershedSegmentationNPP](./Samples/watershedSegmentationNPP)** | **[simpleCudaGraphs](./Samples/simpleCudaGraphs)** | **[streamOrderedAllocation](./Samples/streamOrderedAllocation)** |
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**[deviceQuery](./Samples/deviceQuery)** | **[simpleVoteIntrinsics](./Samples/simpleVoteIntrinsics)** | **[simpleCUBLASXT](./Samples/simpleCUBLASXT)** | **[simpleAttributes](./Samples/simpleAttributes)** |
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**[cudaNvSci](./Samples/cudaNvSci)** | **[tf32TensorCoreGemm](./Samples/tf32TensorCoreGemm)** | **[UnifiedMemoryPerf](./Samples/UnifiedMemoryPerf)** | **[cudaCompressibleMemory](./Samples/cudaCompressibleMemory)** |
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**[bf16TensorCoreGemm](./Samples/bf16TensorCoreGemm)** | **[cuSolverDn_LinearSolver](./Samples/cuSolverDn_LinearSolver)** | **[vulkanImageCUDA](./Samples/vulkanImageCUDA)** | **[conjugateGradientMultiDeviceCG](./Samples/conjugateGradientMultiDeviceCG)** |
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**[matrixMulDrv](./Samples/matrixMulDrv)** | **[cuSolverSp_LinearSolver](./Samples/cuSolverSp_LinearSolver)** | **[simpleCUFFT](./Samples/simpleCUFFT)** | **[reduction](./Samples/reduction)** |
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**[nvJPEG_encoder](./Samples/nvJPEG_encoder)** | **[simpleDrvRuntime](./Samples/simpleDrvRuntime)** | **[MersenneTwisterGP11213](./Samples/MersenneTwisterGP11213)** | **[simpleAWBarrier](./Samples/simpleAWBarrier)** |
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**[immaTensorCoreGemm](./Samples/immaTensorCoreGemm)** | **[bandwidthTest](./Samples/bandwidthTest)** | **[concurrentKernels](./Samples/concurrentKernels)** | **[simpleCUBLAS](./Samples/simpleCUBLAS)** |
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**[NV12toBGRandResize](./Samples/NV12toBGRandResize)** | **[simpleGL](./Samples/simpleGL)** | **[cudaTensorCoreGemm](./Samples/cudaTensorCoreGemm)** | **[jacobiCudaGraphs](./Samples/jacobiCudaGraphs)** |
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**[simpleVulkan](./Samples/simpleVulkan)** | **[vectorAdd_nvrtc](./Samples/vectorAdd_nvrtc)** | **[cannyEdgeDetectorNPP](./Samples/cannyEdgeDetectorNPP)** | **[p2pBandwidthLatencyTest](./Samples/p2pBandwidthLatencyTest)** |
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**[simpleVulkanMMAP](./Samples/simpleVulkanMMAP)** | **[cudaOpenMP](./Samples/cudaOpenMP)** | **[matrixMul](./Samples/matrixMul)** | **[systemWideAtomics](./Samples/systemWideAtomics)** |
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**[boxFilterNPP](./Samples/boxFilterNPP)** | **[cannyEdgeDetectorNPP](./Samples/cannyEdgeDetectorNPP)** | **[concurrentKernels](./Samples/concurrentKernels)** | **[conjugateGradientCudaGraphs](./Samples/conjugateGradientCudaGraphs)** |
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**[conjugateGradientMultiBlockCG](./Samples/conjugateGradientMultiBlockCG)** | **[conjugateGradientMultiDeviceCG](./Samples/conjugateGradientMultiDeviceCG)** | **[cudaCompressibleMemory](./Samples/cudaCompressibleMemory)** | **[cudaNvSci](./Samples/cudaNvSci)** |
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**[cudaNvSciNvMedia](./Samples/cudaNvSciNvMedia)** | **[cudaOpenMP](./Samples/cudaOpenMP)** | **[cudaTensorCoreGemm](./Samples/cudaTensorCoreGemm)** | **[cuSolverDn_LinearSolver](./Samples/cuSolverDn_LinearSolver)** |
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**[cuSolverSp_LinearSolver](./Samples/cuSolverSp_LinearSolver)** | **[deviceQuery](./Samples/deviceQuery)** | **[dmmaTensorCoreGemm](./Samples/dmmaTensorCoreGemm)** | **[EGLStream_CUDA_Interop](./Samples/EGLStream_CUDA_Interop)** |
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**[globalToShmemAsyncCopy](./Samples/globalToShmemAsyncCopy)** | **[immaTensorCoreGemm](./Samples/immaTensorCoreGemm)** | **[jacobiCudaGraphs](./Samples/jacobiCudaGraphs)** | **[matrixMul](./Samples/matrixMul)** |
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**[matrixMulDrv](./Samples/matrixMulDrv)** | **[memMapIPCDrv](./Samples/memMapIPCDrv)** | **[MersenneTwisterGP11213](./Samples/MersenneTwisterGP11213)** | **[NV12toBGRandResize](./Samples/NV12toBGRandResize)** |
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**[nvJPEG](./Samples/nvJPEG)** | **[nvJPEG_encoder](./Samples/nvJPEG_encoder)** | **[p2pBandwidthLatencyTest](./Samples/p2pBandwidthLatencyTest)** | **[reduction](./Samples/reduction)** |
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**[shfl_scan](./Samples/shfl_scan)** | **[simpleAttributes](./Samples/simpleAttributes)** | **[simpleAWBarrier](./Samples/simpleAWBarrier)** | **[simpleCUBLAS](./Samples/simpleCUBLAS)** |
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**[simpleCUBLASXT](./Samples/simpleCUBLASXT)** | **[simpleCUBLAS_LU](./Samples/simpleCUBLAS_LU)** | **[simpleCudaGraphs](./Samples/simpleCudaGraphs)** | **[simpleCUFFT](./Samples/simpleCUFFT)** |
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**[simpleDrvRuntime](./Samples/simpleDrvRuntime)** | **[simpleGL](./Samples/simpleGL)** | **[simpleIPC](./Samples/simpleIPC)** | **[simpleVoteIntrinsics](./Samples/simpleVoteIntrinsics)** |
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**[simpleVulkan](./Samples/simpleVulkan)** | **[simpleVulkanMMAP](./Samples/simpleVulkanMMAP)** | **[simpleZeroCopy](./Samples/simpleZeroCopy)** | **[streamOrderedAllocation](./Samples/streamOrderedAllocation)** |
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**[streamOrderedAllocationIPC](./Samples/streamOrderedAllocationIPC)** | **[streamOrderedAllocationP2P](./Samples/streamOrderedAllocationP2P)** | **[systemWideAtomics](./Samples/systemWideAtomics)** | **[tf32TensorCoreGemm](./Samples/tf32TensorCoreGemm)** |
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**[UnifiedMemoryPerf](./Samples/UnifiedMemoryPerf)** | **[vectorAddMMAP](./Samples/vectorAddMMAP)** | **[vectorAdd_nvrtc](./Samples/vectorAdd_nvrtc)** | **[vulkanImageCUDA](./Samples/vulkanImageCUDA)** |
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**[warpAggregatedAtomicsCG](./Samples/warpAggregatedAtomicsCG)** | **[watershedSegmentationNPP](./Samples/watershedSegmentationNPP)** |
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#### Windows
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**[warpAggregatedAtomicsCG](./Samples/warpAggregatedAtomicsCG)** | **[boxFilterNPP](./Samples/boxFilterNPP)** | **[streamOrderedAllocationP2P](./Samples/streamOrderedAllocationP2P)** | **[binaryPartitionCG](./Samples/binaryPartitionCG)** |
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**[bandwidthTest](./Samples/bandwidthTest)** | **[batchedLabelMarkersAndLabelCompressionNPP](./Samples/batchedLabelMarkersAndLabelCompressionNPP)** | **[bf16TensorCoreGemm](./Samples/bf16TensorCoreGemm)** | **[binaryPartitionCG](./Samples/binaryPartitionCG)** |
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---|---|---|---|
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**[dmmaTensorCoreGemm](./Samples/dmmaTensorCoreGemm)** | **[conjugateGradientMultiBlockCG](./Samples/conjugateGradientMultiBlockCG)** | **[simpleIPC](./Samples/simpleIPC)** | **[memMapIPCDrv](./Samples/memMapIPCDrv)** |
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**[vectorAddMMAP](./Samples/vectorAddMMAP)** | **[shfl_scan](./Samples/shfl_scan)** | **[simpleZeroCopy](./Samples/simpleZeroCopy)** | **[conjugateGradientCudaGraphs](./Samples/conjugateGradientCudaGraphs)** |
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**[globalToShmemAsyncCopy](./Samples/globalToShmemAsyncCopy)** | **[nvJPEG](./Samples/nvJPEG)** | **[batchedLabelMarkersAndLabelCompressionNPP](./Samples/batchedLabelMarkersAndLabelCompressionNPP)** | **[simpleD3D12](./Samples/simpleD3D12)** |
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**[watershedSegmentationNPP](./Samples/watershedSegmentationNPP)** | **[simpleCudaGraphs](./Samples/simpleCudaGraphs)** | **[streamOrderedAllocation](./Samples/streamOrderedAllocation)** | **[deviceQuery](./Samples/deviceQuery)** |
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**[simpleVoteIntrinsics](./Samples/simpleVoteIntrinsics)** | **[simpleCUBLASXT](./Samples/simpleCUBLASXT)** | **[simpleAttributes](./Samples/simpleAttributes)** | **[tf32TensorCoreGemm](./Samples/tf32TensorCoreGemm)** |
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**[UnifiedMemoryPerf](./Samples/UnifiedMemoryPerf)** | **[cudaCompressibleMemory](./Samples/cudaCompressibleMemory)** | **[bf16TensorCoreGemm](./Samples/bf16TensorCoreGemm)** | **[cuSolverDn_LinearSolver](./Samples/cuSolverDn_LinearSolver)** |
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**[vulkanImageCUDA](./Samples/vulkanImageCUDA)** | **[conjugateGradientMultiDeviceCG](./Samples/conjugateGradientMultiDeviceCG)** | **[matrixMulDrv](./Samples/matrixMulDrv)** | **[cuSolverSp_LinearSolver](./Samples/cuSolverSp_LinearSolver)** |
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**[simpleCUFFT](./Samples/simpleCUFFT)** | **[reduction](./Samples/reduction)** | **[nvJPEG_encoder](./Samples/nvJPEG_encoder)** | **[simpleDrvRuntime](./Samples/simpleDrvRuntime)** |
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**[simpleD3D11](./Samples/simpleD3D11)** | **[MersenneTwisterGP11213](./Samples/MersenneTwisterGP11213)** | **[simpleAWBarrier](./Samples/simpleAWBarrier)** | **[immaTensorCoreGemm](./Samples/immaTensorCoreGemm)** |
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**[bandwidthTest](./Samples/bandwidthTest)** | **[concurrentKernels](./Samples/concurrentKernels)** | **[simpleCUBLAS](./Samples/simpleCUBLAS)** | **[NV12toBGRandResize](./Samples/NV12toBGRandResize)** |
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**[simpleGL](./Samples/simpleGL)** | **[cudaTensorCoreGemm](./Samples/cudaTensorCoreGemm)** | **[jacobiCudaGraphs](./Samples/jacobiCudaGraphs)** | **[simpleVulkan](./Samples/simpleVulkan)** |
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**[vectorAdd_nvrtc](./Samples/vectorAdd_nvrtc)** | **[cannyEdgeDetectorNPP](./Samples/cannyEdgeDetectorNPP)** | **[p2pBandwidthLatencyTest](./Samples/p2pBandwidthLatencyTest)** | **[simpleVulkanMMAP](./Samples/simpleVulkanMMAP)** |
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**[cudaOpenMP](./Samples/cudaOpenMP)** | **[matrixMul](./Samples/matrixMul)** |
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**[boxFilterNPP](./Samples/boxFilterNPP)** | **[cannyEdgeDetectorNPP](./Samples/cannyEdgeDetectorNPP)** | **[concurrentKernels](./Samples/concurrentKernels)** | **[conjugateGradientCudaGraphs](./Samples/conjugateGradientCudaGraphs)** |
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**[conjugateGradientMultiBlockCG](./Samples/conjugateGradientMultiBlockCG)** | **[conjugateGradientMultiDeviceCG](./Samples/conjugateGradientMultiDeviceCG)** | **[cudaCompressibleMemory](./Samples/cudaCompressibleMemory)** | **[cudaOpenMP](./Samples/cudaOpenMP)** |
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**[cudaTensorCoreGemm](./Samples/cudaTensorCoreGemm)** | **[cuSolverDn_LinearSolver](./Samples/cuSolverDn_LinearSolver)** | **[cuSolverSp_LinearSolver](./Samples/cuSolverSp_LinearSolver)** | **[deviceQuery](./Samples/deviceQuery)** |
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**[dmmaTensorCoreGemm](./Samples/dmmaTensorCoreGemm)** | **[globalToShmemAsyncCopy](./Samples/globalToShmemAsyncCopy)** | **[immaTensorCoreGemm](./Samples/immaTensorCoreGemm)** | **[jacobiCudaGraphs](./Samples/jacobiCudaGraphs)** |
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**[matrixMul](./Samples/matrixMul)** | **[matrixMulDrv](./Samples/matrixMulDrv)** | **[memMapIPCDrv](./Samples/memMapIPCDrv)** | **[MersenneTwisterGP11213](./Samples/MersenneTwisterGP11213)** |
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**[NV12toBGRandResize](./Samples/NV12toBGRandResize)** | **[nvJPEG](./Samples/nvJPEG)** | **[nvJPEG_encoder](./Samples/nvJPEG_encoder)** | **[p2pBandwidthLatencyTest](./Samples/p2pBandwidthLatencyTest)** |
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**[reduction](./Samples/reduction)** | **[shfl_scan](./Samples/shfl_scan)** | **[simpleAttributes](./Samples/simpleAttributes)** | **[simpleAWBarrier](./Samples/simpleAWBarrier)** |
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**[simpleCUBLAS](./Samples/simpleCUBLAS)** | **[simpleCUBLASXT](./Samples/simpleCUBLASXT)** | **[simpleCUBLAS_LU](./Samples/simpleCUBLAS_LU)** | **[simpleCudaGraphs](./Samples/simpleCudaGraphs)** |
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**[simpleCUFFT](./Samples/simpleCUFFT)** | **[simpleD3D11](./Samples/simpleD3D11)** | **[simpleD3D12](./Samples/simpleD3D12)** | **[simpleDrvRuntime](./Samples/simpleDrvRuntime)** |
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**[simpleGL](./Samples/simpleGL)** | **[simpleIPC](./Samples/simpleIPC)** | **[simpleVoteIntrinsics](./Samples/simpleVoteIntrinsics)** | **[simpleVulkan](./Samples/simpleVulkan)** |
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**[simpleVulkanMMAP](./Samples/simpleVulkanMMAP)** | **[simpleZeroCopy](./Samples/simpleZeroCopy)** | **[streamOrderedAllocation](./Samples/streamOrderedAllocation)** | **[streamOrderedAllocationP2P](./Samples/streamOrderedAllocationP2P)** |
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**[tf32TensorCoreGemm](./Samples/tf32TensorCoreGemm)** | **[UnifiedMemoryPerf](./Samples/UnifiedMemoryPerf)** | **[vectorAddMMAP](./Samples/vectorAddMMAP)** | **[vectorAdd_nvrtc](./Samples/vectorAdd_nvrtc)** |
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**[vulkanImageCUDA](./Samples/vulkanImageCUDA)** | **[warpAggregatedAtomicsCG](./Samples/warpAggregatedAtomicsCG)** | **[watershedSegmentationNPP](./Samples/watershedSegmentationNPP)** |
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## Dependencies
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@ -285,6 +285,12 @@ ifeq ($(TARGET_OS),android)
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SAMPLE_ENABLED := 0
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endif
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# This sample is not supported on QNX
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ifeq ($(TARGET_OS),qnx)
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$(info >>> WARNING - EGLStream_CUDA_Interop is not supported on QNX - waiving sample <<<)
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SAMPLE_ENABLED := 0
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endif
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ALL_LDFLAGS :=
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ALL_LDFLAGS += $(ALL_CCFLAGS)
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ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
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@ -30,7 +30,7 @@ cuDeviceGet, cuDeviceGetAttribute, cuDeviceComputeCapability, cuDeviceGetCount,
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## Prerequisites
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Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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Make sure the dependencies mentioned in [Dependencies]() section above are installed.
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## Build and Run
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@ -263,6 +263,14 @@ ALL_CCFLAGS += $(EXTRA_NVCCFLAGS)
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ALL_CCFLAGS += $(addprefix -Xcompiler ,$(CCFLAGS))
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ALL_CCFLAGS += $(addprefix -Xcompiler ,$(EXTRA_CCFLAGS))
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SAMPLE_ENABLED := 1
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# This sample is not supported on QNX
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ifeq ($(TARGET_OS),qnx)
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$(info >>> WARNING - MersenneTwisterGP11213 is not supported on QNX - waiving sample <<<)
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SAMPLE_ENABLED := 0
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endif
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ALL_LDFLAGS :=
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ALL_LDFLAGS += $(ALL_CCFLAGS)
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ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
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@ -297,6 +305,10 @@ ALL_CCFLAGS += --threads 0
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LIBRARIES += -lcurand_static -lculibos
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ifeq ($(SAMPLE_ENABLED),0)
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EXEC ?= @echo "[@]"
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endif
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################################################################################
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# Target rules
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@ -304,16 +316,23 @@ all: build
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build: MersenneTwisterGP11213
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check.deps:
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ifeq ($(SAMPLE_ENABLED),0)
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@echo "Sample will be waived due to the above missing dependencies"
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else
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@echo "Sample is ready - all dependencies have been met"
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endif
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MersenneTwister.o:MersenneTwister.cpp
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$(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -c $<
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$(EXEC) $(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -c $<
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MersenneTwisterGP11213: MersenneTwister.o
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$(NVCC) $(ALL_LDFLAGS) $(GENCODE_FLAGS) -o $@ $+ $(LIBRARIES)
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mkdir -p ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
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cp $@ ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
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$(EXEC) $(NVCC) $(ALL_LDFLAGS) $(GENCODE_FLAGS) -o $@ $+ $(LIBRARIES)
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$(EXEC) mkdir -p ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
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$(EXEC) cp $@ ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
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run: build
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./MersenneTwisterGP11213
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$(EXEC) ./MersenneTwisterGP11213
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clean:
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rm -f MersenneTwisterGP11213 MersenneTwister.o
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@ -53,8 +53,7 @@ const unsigned int DEFAULT_SEED = 777;
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///////////////////////////////////////////////////////////////////////////////
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// Main program
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///////////////////////////////////////////////////////////////////////////////
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int main(int argc, char **argv)
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{
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int main(int argc, char **argv) {
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// Start logs
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printf("%s Starting...\n\n", argv[0]);
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@ -65,9 +64,8 @@ int main(int argc, char **argv)
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// parsing the number of random numbers to generate
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int rand_n = DEFAULT_RAND_N;
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if (checkCmdLineFlag(argc, (const char **) argv, "count"))
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{
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rand_n = getCmdLineArgumentInt(argc, (const char **) argv, "count");
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if (checkCmdLineFlag(argc, (const char **)argv, "count")) {
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rand_n = getCmdLineArgumentInt(argc, (const char **)argv, "count");
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}
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printf("Allocating data for %i samples...\n", rand_n);
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@ -75,9 +73,8 @@ int main(int argc, char **argv)
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// parsing the seed
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int seed = DEFAULT_SEED;
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if (checkCmdLineFlag(argc, (const char **) argv, "seed"))
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{
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seed = getCmdLineArgumentInt(argc, (const char **) argv, "seed");
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if (checkCmdLineFlag(argc, (const char **)argv, "seed")) {
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seed = getCmdLineArgumentInt(argc, (const char **)argv, "seed");
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}
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printf("Seeding with %i ...\n", seed);
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@ -94,24 +91,26 @@ int main(int argc, char **argv)
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checkCudaErrors(curandSetPseudoRandomGeneratorSeed(prngGPU, seed));
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curandGenerator_t prngCPU;
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checkCudaErrors(curandCreateGeneratorHost(&prngCPU, CURAND_RNG_PSEUDO_MTGP32));
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checkCudaErrors(
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curandCreateGeneratorHost(&prngCPU, CURAND_RNG_PSEUDO_MTGP32));
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checkCudaErrors(curandSetPseudoRandomGeneratorSeed(prngCPU, seed));
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//
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// Example 1: Compare random numbers generated on GPU and CPU
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float *h_RandGPU = (float *)malloc(rand_n * sizeof(float));
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float *h_RandGPU;
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checkCudaErrors(cudaMallocHost(&h_RandGPU, rand_n * sizeof(float)));
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printf("Generating random numbers on GPU...\n\n");
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checkCudaErrors(curandGenerateUniform(prngGPU, (float *) d_Rand, rand_n));
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checkCudaErrors(curandGenerateUniform(prngGPU, (float *)d_Rand, rand_n));
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printf("\nReading back the results...\n");
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checkCudaErrors(cudaMemcpyAsync(h_RandGPU, d_Rand, rand_n * sizeof(float), cudaMemcpyDeviceToHost, stream));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(h_RandGPU, d_Rand, rand_n * sizeof(float),
|
||||
cudaMemcpyDeviceToHost, stream));
|
||||
|
||||
float *h_RandCPU = (float *)malloc(rand_n * sizeof(float));
|
||||
|
||||
printf("Generating random numbers on CPU...\n\n");
|
||||
checkCudaErrors(curandGenerateUniform(prngCPU, (float *) h_RandCPU, rand_n));
|
||||
checkCudaErrors(curandGenerateUniform(prngCPU, (float *)h_RandCPU, rand_n));
|
||||
|
||||
checkCudaErrors(cudaStreamSynchronize(stream));
|
||||
printf("Comparing CPU/GPU random numbers...\n\n");
|
||||
|
|
@ -127,17 +126,18 @@ int main(int argc, char **argv)
|
|||
sdkResetTimer(&hTimer);
|
||||
sdkStartTimer(&hTimer);
|
||||
|
||||
for (i = 0; i < numIterations; i++)
|
||||
{
|
||||
checkCudaErrors(curandGenerateUniform(prngGPU, (float *) d_Rand, rand_n));
|
||||
for (i = 0; i < numIterations; i++) {
|
||||
checkCudaErrors(curandGenerateUniform(prngGPU, (float *)d_Rand, rand_n));
|
||||
}
|
||||
|
||||
checkCudaErrors(cudaStreamSynchronize(stream));
|
||||
sdkStopTimer(&hTimer);
|
||||
|
||||
double gpuTime = 1.0e-3 * sdkGetTimerValue(&hTimer)/(double)numIterations;
|
||||
double gpuTime = 1.0e-3 * sdkGetTimerValue(&hTimer) / (double)numIterations;
|
||||
|
||||
printf("MersenneTwisterGP11213, Throughput = %.4f GNumbers/s, Time = %.5f s, Size = %u Numbers\n",
|
||||
printf(
|
||||
"MersenneTwisterGP11213, Throughput = %.4f GNumbers/s, Time = %.5f s, "
|
||||
"Size = %u Numbers\n",
|
||||
1.0e-9 * rand_n / gpuTime, gpuTime, rand_n);
|
||||
|
||||
printf("Shutting down...\n");
|
||||
|
|
@ -147,31 +147,27 @@ int main(int argc, char **argv)
|
|||
checkCudaErrors(cudaStreamDestroy(stream));
|
||||
checkCudaErrors(cudaFree(d_Rand));
|
||||
sdkDeleteTimer(&hTimer);
|
||||
free(h_RandGPU);
|
||||
checkCudaErrors(cudaFreeHost(h_RandGPU));
|
||||
free(h_RandCPU);
|
||||
|
||||
exit(L1norm < 1e-6 ? EXIT_SUCCESS : EXIT_FAILURE);
|
||||
}
|
||||
|
||||
|
||||
float compareResults(int rand_n, float *h_RandGPU, float *h_RandCPU)
|
||||
{
|
||||
float compareResults(int rand_n, float *h_RandGPU, float *h_RandCPU) {
|
||||
int i;
|
||||
float rCPU, rGPU, delta;
|
||||
float max_delta = 0.;
|
||||
float sum_delta = 0.;
|
||||
float sum_ref = 0.;
|
||||
|
||||
for (i = 0; i < rand_n; i++)
|
||||
{
|
||||
for (i = 0; i < rand_n; i++) {
|
||||
rCPU = h_RandCPU[i];
|
||||
rGPU = h_RandGPU[i];
|
||||
delta = fabs(rCPU - rGPU);
|
||||
sum_delta += delta;
|
||||
sum_ref += fabs(rCPU);
|
||||
|
||||
if (delta >= max_delta)
|
||||
{
|
||||
if (delta >= max_delta) {
|
||||
max_delta = delta;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ x86_64, ppc64le, armv7l
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -113,6 +113,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -109,6 +109,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cudaMemcpy2D, cudaMallocManaged
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ cudaMallocManaged, cudaStreamAttachMemAsync, cudaMemcpyAsync, cudaMallocHost, cu
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -111,6 +111,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -107,6 +107,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cudaSetDevice, cudaHostAlloc, cudaFree, cudaMallocHost, cudaFreeHost, cudaMemcpy
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -271,6 +271,12 @@ ifeq ($(TARGET_OS),darwin)
|
|||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
# This sample is not supported on QNX
|
||||
ifeq ($(TARGET_OS),qnx)
|
||||
$(info >>> WARNING - batchedLabelMarkersAndLabelCompressionNPP is not supported on QNX - waiving sample <<<)
|
||||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
ALL_LDFLAGS :=
|
||||
ALL_LDFLAGS += $(ALL_CCFLAGS)
|
||||
ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ x86_64, ppc64le, armv7l
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -36,7 +36,9 @@
|
|||
#include <string.h>
|
||||
#include <fstream>
|
||||
|
||||
#include <cuda_runtime.h>
|
||||
#include <helper_cuda.h>
|
||||
#include <helper_string.h>
|
||||
#include <npp.h>
|
||||
|
||||
// Note: If you want to view these images we HIGHLY recommend using imagej
|
||||
|
|
@ -102,11 +104,12 @@ void tearDown() // Clean up and tear down
|
|||
if (pUFBatchSrcDstImageListDev != 0) cudaFree(pUFBatchSrcDstImageListDev);
|
||||
if (pUFBatchSrcImageListDev != 0) cudaFree(pUFBatchSrcImageListDev);
|
||||
if (pUFBatchPerImageCompressedCountListHost != 0)
|
||||
free(pUFBatchPerImageCompressedCountListHost);
|
||||
cudaFreeHost(pUFBatchPerImageCompressedCountListHost);
|
||||
if (pUFBatchSrcDstScratchBufferListHost != 0)
|
||||
free(pUFBatchSrcDstScratchBufferListHost);
|
||||
if (pUFBatchSrcDstImageListHost != 0) free(pUFBatchSrcDstImageListHost);
|
||||
if (pUFBatchSrcImageListHost != 0) free(pUFBatchSrcImageListHost);
|
||||
cudaFreeHost(pUFBatchSrcDstScratchBufferListHost);
|
||||
if (pUFBatchSrcDstImageListHost != 0)
|
||||
cudaFreeHost(pUFBatchSrcDstImageListHost);
|
||||
if (pUFBatchSrcImageListHost != 0) cudaFreeHost(pUFBatchSrcImageListHost);
|
||||
|
||||
for (int j = 0; j < NUMBER_OF_IMAGES; j++) {
|
||||
if (pUFCompressedLabelsScratchBufferDev[j] != 0)
|
||||
|
|
@ -115,8 +118,8 @@ void tearDown() // Clean up and tear down
|
|||
cudaFree(pUFGenerateLabelsScratchBufferDev[j]);
|
||||
if (pUFLabelDev[j] != 0) cudaFree(pUFLabelDev[j]);
|
||||
if (pInputImageDev[j] != 0) cudaFree(pInputImageDev[j]);
|
||||
if (pUFLabelHost[j] != 0) free(pUFLabelHost[j]);
|
||||
if (pInputImageHost[j] != 0) free(pInputImageHost[j]);
|
||||
if (pUFLabelHost[j] != 0) cudaFreeHost(pUFLabelHost[j]);
|
||||
if (pInputImageHost[j] != 0) cudaFreeHost(pInputImageHost[j]);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -177,7 +180,7 @@ int loadRaw8BitImage(Npp8u *pImage, int nWidth, int nHeight, int nImage) {
|
|||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
bmpFile = fopen(InputFile, "rb");
|
||||
FOPEN(bmpFile, InputFile, "rb");
|
||||
} else if (nImage == 1) {
|
||||
if (nWidth != 512 || nHeight != 512) return -1;
|
||||
const char *fileName = "CT_skull_512x512_8u.raw";
|
||||
|
|
@ -187,7 +190,7 @@ int loadRaw8BitImage(Npp8u *pImage, int nWidth, int nHeight, int nImage) {
|
|||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
bmpFile = fopen(InputFile, "rb");
|
||||
FOPEN(bmpFile, InputFile, "rb");
|
||||
} else if (nImage == 2) {
|
||||
if (nWidth != 509 || nHeight != 335) return -1;
|
||||
const char *fileName = "PCB_METAL_509x335_8u.raw";
|
||||
|
|
@ -197,7 +200,7 @@ int loadRaw8BitImage(Npp8u *pImage, int nWidth, int nHeight, int nImage) {
|
|||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
bmpFile = fopen(InputFile, "rb");
|
||||
FOPEN(bmpFile, InputFile, "rb");
|
||||
} else if (nImage == 3) {
|
||||
if (nWidth != 1024 || nHeight != 683) return -1;
|
||||
const char *fileName = "PCB2_1024x683_8u.raw";
|
||||
|
|
@ -207,7 +210,7 @@ int loadRaw8BitImage(Npp8u *pImage, int nWidth, int nHeight, int nImage) {
|
|||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
bmpFile = fopen(InputFile, "rb");
|
||||
FOPEN(bmpFile, InputFile, "rb");
|
||||
} else if (nImage == 4) {
|
||||
if (nWidth != 1280 || nHeight != 720) return -1;
|
||||
const char *fileName = "PCB_1280x720_8u.raw";
|
||||
|
|
@ -217,7 +220,7 @@ int loadRaw8BitImage(Npp8u *pImage, int nWidth, int nHeight, int nImage) {
|
|||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
bmpFile = fopen(InputFile, "rb");
|
||||
FOPEN(bmpFile, InputFile, "rb");
|
||||
} else {
|
||||
printf("Input file load failed.\n");
|
||||
return -1;
|
||||
|
|
@ -347,9 +350,11 @@ int main(int argc, char **argv) {
|
|||
oSizeROI[nImage].width * sizeof(Npp32u) * oSizeROI[nImage].height);
|
||||
if (cudaError != cudaSuccess) return NPP_MEMORY_ALLOCATION_ERR;
|
||||
|
||||
pInputImageHost[nImage] = reinterpret_cast<Npp8u *>(malloc(
|
||||
checkCudaErrors(cudaMallocHost(
|
||||
&(pInputImageHost[nImage]),
|
||||
oSizeROI[nImage].width * sizeof(Npp8u) * oSizeROI[nImage].height));
|
||||
pUFLabelHost[nImage] = reinterpret_cast<Npp32u *>(malloc(
|
||||
checkCudaErrors(cudaMallocHost(
|
||||
&(pUFLabelHost[nImage]),
|
||||
oSizeROI[nImage].width * sizeof(Npp32u) * oSizeROI[nImage].height));
|
||||
|
||||
// Use UF functions throughout this sample.
|
||||
|
|
@ -409,15 +414,15 @@ int main(int argc, char **argv) {
|
|||
}
|
||||
|
||||
if (nImage == 0)
|
||||
bmpFile = fopen(LabelMarkersOutputFile0.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersOutputFile0.c_str(), "wb");
|
||||
else if (nImage == 1)
|
||||
bmpFile = fopen(LabelMarkersOutputFile1.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersOutputFile1.c_str(), "wb");
|
||||
else if (nImage == 2)
|
||||
bmpFile = fopen(LabelMarkersOutputFile2.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersOutputFile2.c_str(), "wb");
|
||||
else if (nImage == 3)
|
||||
bmpFile = fopen(LabelMarkersOutputFile3.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersOutputFile3.c_str(), "wb");
|
||||
else if (nImage == 4)
|
||||
bmpFile = fopen(LabelMarkersOutputFile4.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersOutputFile4.c_str(), "wb");
|
||||
|
||||
if (bmpFile == NULL) return -1;
|
||||
size_t nSize = 0;
|
||||
|
|
@ -478,15 +483,15 @@ int main(int argc, char **argv) {
|
|||
}
|
||||
|
||||
if (nImage == 0)
|
||||
bmpFile = fopen(CompressedMarkerLabelsOutputFile0.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsOutputFile0.c_str(), "wb");
|
||||
else if (nImage == 1)
|
||||
bmpFile = fopen(CompressedMarkerLabelsOutputFile1.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsOutputFile1.c_str(), "wb");
|
||||
else if (nImage == 2)
|
||||
bmpFile = fopen(CompressedMarkerLabelsOutputFile2.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsOutputFile2.c_str(), "wb");
|
||||
else if (nImage == 3)
|
||||
bmpFile = fopen(CompressedMarkerLabelsOutputFile3.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsOutputFile3.c_str(), "wb");
|
||||
else if (nImage == 4)
|
||||
bmpFile = fopen(CompressedMarkerLabelsOutputFile4.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsOutputFile4.c_str(), "wb");
|
||||
|
||||
if (bmpFile == NULL) return -1;
|
||||
nSize = 0;
|
||||
|
|
@ -554,10 +559,11 @@ int main(int argc, char **argv) {
|
|||
cudaMalloc((void **)&pUFBatchSrcDstImageListDev, nBatchImageListBytes);
|
||||
if (cudaError != cudaSuccess) return NPP_MEMORY_ALLOCATION_ERR;
|
||||
|
||||
pUFBatchSrcImageListHost =
|
||||
reinterpret_cast<NppiImageDescriptor *>(malloc(nBatchImageListBytes));
|
||||
pUFBatchSrcDstImageListHost =
|
||||
reinterpret_cast<NppiImageDescriptor *>(malloc(nBatchImageListBytes));
|
||||
checkCudaErrors(
|
||||
cudaMallocHost((void **)&pUFBatchSrcImageListHost, nBatchImageListBytes));
|
||||
|
||||
checkCudaErrors(cudaMallocHost((void **)&pUFBatchSrcDstImageListHost,
|
||||
nBatchImageListBytes));
|
||||
|
||||
NppiSize oMaxROISize = {0, 0};
|
||||
|
||||
|
|
@ -620,15 +626,15 @@ int main(int argc, char **argv) {
|
|||
// Save output to files
|
||||
for (int nImage = 0; nImage < NUMBER_OF_IMAGES; nImage++) {
|
||||
if (nImage == 0)
|
||||
bmpFile = fopen(LabelMarkersBatchOutputFile0.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersBatchOutputFile0.c_str(), "wb");
|
||||
else if (nImage == 1)
|
||||
bmpFile = fopen(LabelMarkersBatchOutputFile1.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersBatchOutputFile1.c_str(), "wb");
|
||||
else if (nImage == 2)
|
||||
bmpFile = fopen(LabelMarkersBatchOutputFile2.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersBatchOutputFile2.c_str(), "wb");
|
||||
else if (nImage == 3)
|
||||
bmpFile = fopen(LabelMarkersBatchOutputFile3.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersBatchOutputFile3.c_str(), "wb");
|
||||
else if (nImage == 4)
|
||||
bmpFile = fopen(LabelMarkersBatchOutputFile4.c_str(), "wb");
|
||||
FOPEN(bmpFile, LabelMarkersBatchOutputFile4.c_str(), "wb");
|
||||
|
||||
if (bmpFile == NULL) return -1;
|
||||
size_t nSize = 0;
|
||||
|
|
@ -652,12 +658,13 @@ int main(int argc, char **argv) {
|
|||
|
||||
// Allocate host side scratch buffer point and size list and initialize with
|
||||
// device scratch buffer pointers
|
||||
pUFBatchSrcDstScratchBufferListHost =
|
||||
reinterpret_cast<NppiBufferDescriptor *>(
|
||||
malloc(NUMBER_OF_IMAGES * sizeof(NppiBufferDescriptor)));
|
||||
checkCudaErrors(
|
||||
cudaMallocHost((void **)&pUFBatchSrcDstScratchBufferListHost,
|
||||
NUMBER_OF_IMAGES * sizeof(NppiBufferDescriptor)));
|
||||
|
||||
pUFBatchPerImageCompressedCountListHost =
|
||||
reinterpret_cast<Npp32u *>(malloc(NUMBER_OF_IMAGES * sizeof(Npp32u)));
|
||||
checkCudaErrors(
|
||||
cudaMallocHost((void **)&pUFBatchPerImageCompressedCountListHost,
|
||||
+NUMBER_OF_IMAGES * sizeof(Npp32u)));
|
||||
|
||||
// Start buffer pointer at beginning of full per image buffer list sized
|
||||
// pUFCompressedLabelsScratchBufferDev[0]
|
||||
|
|
@ -728,15 +735,15 @@ int main(int argc, char **argv) {
|
|||
// Save compressed label images into files
|
||||
for (int nImage = 0; nImage < NUMBER_OF_IMAGES; nImage++) {
|
||||
if (nImage == 0)
|
||||
bmpFile = fopen(CompressedMarkerLabelsBatchOutputFile0.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsBatchOutputFile0.c_str(), "wb");
|
||||
else if (nImage == 1)
|
||||
bmpFile = fopen(CompressedMarkerLabelsBatchOutputFile1.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsBatchOutputFile1.c_str(), "wb");
|
||||
else if (nImage == 2)
|
||||
bmpFile = fopen(CompressedMarkerLabelsBatchOutputFile2.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsBatchOutputFile2.c_str(), "wb");
|
||||
else if (nImage == 3)
|
||||
bmpFile = fopen(CompressedMarkerLabelsBatchOutputFile3.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsBatchOutputFile3.c_str(), "wb");
|
||||
else if (nImage == 4)
|
||||
bmpFile = fopen(CompressedMarkerLabelsBatchOutputFile4.c_str(), "wb");
|
||||
FOPEN(bmpFile, CompressedMarkerLabelsBatchOutputFile4.c_str(), "wb");
|
||||
|
||||
if (bmpFile == NULL) return -1;
|
||||
size_t nSize = 0;
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cudaMallocManaged, cudaDeviceSynchronize, cudaFuncSetAttribute, cudaEventCreate,
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ x86_64, ppc64le, armv7l
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -31,14 +31,16 @@
|
|||
* 1.) Each thread loads a value from random array.
|
||||
* 2.) then checks if it is odd or even.
|
||||
* 3.) create binary partition group based on the above predicate
|
||||
* 4.) we count the number of odd/even in the group based on size of the binary groups
|
||||
* 4.) we count the number of odd/even in the group based on size of the binary
|
||||
groups
|
||||
* 5.) write it global counter of odd.
|
||||
* 6.) sum the values loaded by individual threads(using reduce) and write it to global
|
||||
* even & odd elements sum.
|
||||
* 6.) sum the values loaded by individual threads(using reduce) and write it to
|
||||
global even & odd elements sum.
|
||||
*
|
||||
* **NOTE** : binary_partition results in splitting warp into divergent thread groups
|
||||
this is not good from performance perspective, but in cases where warp
|
||||
divergence is inevitable one can use binary_partition group.
|
||||
* **NOTE** :
|
||||
* binary_partition results in splitting warp into divergent thread groups
|
||||
* this is not good from performance perspective, but in cases where warp
|
||||
* divergence is inevitable one can use binary_partition group.
|
||||
*/
|
||||
|
||||
#include <stdio.h>
|
||||
|
|
@ -48,50 +50,42 @@
|
|||
|
||||
namespace cg = cooperative_groups;
|
||||
|
||||
void initOddEvenArr(int *inputArr, unsigned int size)
|
||||
{
|
||||
for (int i=0; i < size; i++)
|
||||
{
|
||||
void initOddEvenArr(int *inputArr, unsigned int size) {
|
||||
for (int i = 0; i < size; i++) {
|
||||
inputArr[i] = rand() % 50;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* CUDA kernel device code
|
||||
*
|
||||
* Creates cooperative groups and performs odd/even counting & summation.
|
||||
*/
|
||||
__global__ void oddEvenCountAndSumCG(int *inputArr, int *numOfOdds, int *sumOfOddAndEvens, unsigned int size)
|
||||
{
|
||||
__global__ void oddEvenCountAndSumCG(int *inputArr, int *numOfOdds,
|
||||
int *sumOfOddAndEvens, unsigned int size) {
|
||||
cg::thread_block cta = cg::this_thread_block();
|
||||
cg::grid_group grid = cg::this_grid();
|
||||
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
|
||||
|
||||
for (int i = grid.thread_rank(); i < size; i += grid.size())
|
||||
{
|
||||
for (int i = grid.thread_rank(); i < size; i += grid.size()) {
|
||||
int elem = inputArr[i];
|
||||
auto subTile = cg::binary_partition(tile32, elem & 1);
|
||||
if (elem & 1) // Odd numbers group
|
||||
{
|
||||
int oddGroupSum = cg::reduce(subTile, elem, cg::plus<int>());
|
||||
|
||||
if (subTile.thread_rank() == 0)
|
||||
{
|
||||
if (subTile.thread_rank() == 0) {
|
||||
// Add number of odds present in this group of Odds.
|
||||
atomicAdd(numOfOdds, subTile.size());
|
||||
|
||||
// Add local reduction of odds present in this group of Odds.
|
||||
atomicAdd(&sumOfOddAndEvens[0], oddGroupSum);
|
||||
|
||||
}
|
||||
}
|
||||
else // Even numbers group
|
||||
} else // Even numbers group
|
||||
{
|
||||
int evenGroupSum = cg::reduce(subTile, elem, cg::plus<int>());
|
||||
|
||||
if (subTile.thread_rank() == 0)
|
||||
{
|
||||
if (subTile.thread_rank() == 0) {
|
||||
// Add local reduction of even present in this group of evens.
|
||||
atomicAdd(&sumOfOddAndEvens[1], evenGroupSum);
|
||||
}
|
||||
|
|
@ -102,50 +96,60 @@ __global__ void oddEvenCountAndSumCG(int *inputArr, int *numOfOdds, int *sumOfOd
|
|||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Host main routine
|
||||
*/
|
||||
int main(int argc, const char **argv)
|
||||
{
|
||||
int main(int argc, const char **argv) {
|
||||
int deviceId = findCudaDevice(argc, argv);
|
||||
int *h_inputArr, *d_inputArr;
|
||||
int *h_numOfOdds, *d_numOfOdds;
|
||||
int *h_sumOfOddEvenElems, *d_sumOfOddEvenElems;
|
||||
unsigned int arrSize = 1024 * 100;
|
||||
|
||||
h_inputArr = new int[arrSize];
|
||||
h_numOfOdds = new int[1];
|
||||
h_sumOfOddEvenElems = new int[2];
|
||||
checkCudaErrors(cudaMallocHost(&h_inputArr, sizeof(int) * arrSize));
|
||||
checkCudaErrors(cudaMallocHost(&h_numOfOdds, sizeof(int)));
|
||||
checkCudaErrors(cudaMallocHost(&h_sumOfOddEvenElems, sizeof(int) * 2));
|
||||
initOddEvenArr(h_inputArr, arrSize);
|
||||
|
||||
cudaStream_t stream;
|
||||
checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
|
||||
checkCudaErrors(cudaMalloc(&d_inputArr, sizeof(int)*arrSize));
|
||||
checkCudaErrors(cudaMalloc(&d_inputArr, sizeof(int) * arrSize));
|
||||
checkCudaErrors(cudaMalloc(&d_numOfOdds, sizeof(int)));
|
||||
checkCudaErrors(cudaMalloc(&d_sumOfOddEvenElems, sizeof(int)*2));
|
||||
checkCudaErrors(cudaMalloc(&d_sumOfOddEvenElems, sizeof(int) * 2));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(d_inputArr, h_inputArr, sizeof(int)*arrSize, cudaMemcpyHostToDevice, stream));
|
||||
checkCudaErrors(cudaMemcpyAsync(d_inputArr, h_inputArr, sizeof(int) * arrSize,
|
||||
cudaMemcpyHostToDevice, stream));
|
||||
checkCudaErrors(cudaMemsetAsync(d_numOfOdds, 0, sizeof(int), stream));
|
||||
checkCudaErrors(cudaMemsetAsync(d_sumOfOddEvenElems, 0, 2*sizeof(int), stream));
|
||||
checkCudaErrors(
|
||||
cudaMemsetAsync(d_sumOfOddEvenElems, 0, 2 * sizeof(int), stream));
|
||||
|
||||
//Launch the kernel
|
||||
int threadsPerBlock=1024;
|
||||
int blocksPerGrid = arrSize / threadsPerBlock;
|
||||
// Launch the kernel
|
||||
int threadsPerBlock = 0;
|
||||
int blocksPerGrid = 0;
|
||||
checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(
|
||||
&blocksPerGrid, &threadsPerBlock, oddEvenCountAndSumCG, 0, 0));
|
||||
|
||||
printf("\nLaunching %d blocks with %d threads...\n\n",blocksPerGrid, threadsPerBlock);
|
||||
printf("\nLaunching %d blocks with %d threads...\n\n", blocksPerGrid,
|
||||
threadsPerBlock);
|
||||
|
||||
oddEvenCountAndSumCG<<<blocksPerGrid, threadsPerBlock, 0, stream>>>(d_inputArr, d_numOfOdds, d_sumOfOddEvenElems, arrSize);
|
||||
oddEvenCountAndSumCG<<<blocksPerGrid, threadsPerBlock, 0, stream>>>(
|
||||
d_inputArr, d_numOfOdds, d_sumOfOddEvenElems, arrSize);
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(h_numOfOdds, d_numOfOdds, sizeof(int), cudaMemcpyDeviceToHost, stream));
|
||||
checkCudaErrors(cudaMemcpyAsync(h_sumOfOddEvenElems, d_sumOfOddEvenElems, 2*sizeof(int), cudaMemcpyDeviceToHost, stream));
|
||||
checkCudaErrors(cudaMemcpyAsync(h_numOfOdds, d_numOfOdds, sizeof(int),
|
||||
cudaMemcpyDeviceToHost, stream));
|
||||
checkCudaErrors(cudaMemcpyAsync(h_sumOfOddEvenElems, d_sumOfOddEvenElems,
|
||||
2 * sizeof(int), cudaMemcpyDeviceToHost,
|
||||
stream));
|
||||
checkCudaErrors(cudaStreamSynchronize(stream));
|
||||
|
||||
printf("Array size = %d Num of Odds = %d Sum of Odds = %d Sum of Evens %d\n", arrSize, h_numOfOdds[0], h_sumOfOddEvenElems[0], h_sumOfOddEvenElems[1]);
|
||||
printf("Array size = %d Num of Odds = %d Sum of Odds = %d Sum of Evens %d\n",
|
||||
arrSize, h_numOfOdds[0], h_sumOfOddEvenElems[0],
|
||||
h_sumOfOddEvenElems[1]);
|
||||
printf("\n...Done.\n\n");
|
||||
|
||||
delete[] h_inputArr;
|
||||
delete[] h_numOfOdds;
|
||||
delete[] h_sumOfOddEvenElems;
|
||||
checkCudaErrors(cudaFreeHost(h_inputArr));
|
||||
checkCudaErrors(cudaFreeHost(h_numOfOdds));
|
||||
checkCudaErrors(cudaFreeHost(h_sumOfOddEvenElems));
|
||||
|
||||
checkCudaErrors(cudaFree(d_inputArr));
|
||||
checkCudaErrors(cudaFree(d_numOfOdds));
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ x86_64, ppc64le, armv7l
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -118,6 +118,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -114,6 +114,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ x86_64, ppc64le, armv7l
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -118,6 +118,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -114,6 +114,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ x86_64, ppc64le, armv7l
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -265,6 +265,12 @@ ALL_CCFLAGS += $(addprefix -Xcompiler ,$(EXTRA_CCFLAGS))
|
|||
|
||||
SAMPLE_ENABLED := 1
|
||||
|
||||
# This sample is not supported on QNX
|
||||
ifeq ($(TARGET_OS),qnx)
|
||||
$(info >>> WARNING - conjugateGradientCudaGraphs is not supported on QNX - waiving sample <<<)
|
||||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
ALL_LDFLAGS :=
|
||||
ALL_LDFLAGS += $(ALL_CCFLAGS)
|
||||
ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cudaStreamBeginCapture, cudaStreamEndCapture, cudaGraphCreate, cudaGraphLaunch,
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -25,7 +25,6 @@
|
|||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
|
||||
/*
|
||||
* This sample implements a conjugate gradient solver on GPU
|
||||
* using CUBLAS and CUSPARSE with CUDA Graphs
|
||||
|
|
@ -46,7 +45,6 @@
|
|||
#include <helper_cuda.h> // helper function CUDA error checking and initialization
|
||||
#include <helper_functions.h> // helper for shared functions common to CUDA Samples
|
||||
|
||||
|
||||
const char *sSDKname = "conjugateGradientCudaGraphs";
|
||||
|
||||
#ifndef WITH_GRAPH
|
||||
|
|
@ -145,12 +143,12 @@ int main(int argc, char **argv) {
|
|||
/* Generate a random tridiagonal symmetric matrix in CSR format */
|
||||
N = 1048576;
|
||||
nz = (N - 2) * 3 + 4;
|
||||
I = (int *)malloc(sizeof(int) * (N + 1));
|
||||
J = (int *)malloc(sizeof(int) * nz);
|
||||
val = (float *)malloc(sizeof(float) * nz);
|
||||
checkCudaErrors(cudaMallocHost(&I, sizeof(int) * (N + 1)));
|
||||
checkCudaErrors(cudaMallocHost(&J, sizeof(int) * nz));
|
||||
checkCudaErrors(cudaMallocHost(&val, sizeof(float) * nz));
|
||||
genTridiag(I, J, val, N, nz);
|
||||
|
||||
x = (float *)malloc(sizeof(float) * N);
|
||||
checkCudaErrors(cudaMallocHost(&x, sizeof(float) * N));
|
||||
rhs = (float *)malloc(sizeof(float) * N);
|
||||
|
||||
for (int i = 0; i < N; i++) {
|
||||
|
|
@ -192,9 +190,9 @@ int main(int argc, char **argv) {
|
|||
|
||||
/* Wrap raw data into cuSPARSE generic API objects */
|
||||
cusparseSpMatDescr_t matA = NULL;
|
||||
checkCudaErrors(cusparseCreateCsr(
|
||||
&matA, N, N, nz, d_row, d_col, d_val, CUSPARSE_INDEX_32I,
|
||||
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F));
|
||||
checkCudaErrors(cusparseCreateCsr(&matA, N, N, nz, d_row, d_col, d_val,
|
||||
CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I,
|
||||
CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F));
|
||||
cusparseDnVecDescr_t vecx = NULL;
|
||||
checkCudaErrors(cusparseCreateDnVec(&vecx, N, d_x, CUDA_R_32F));
|
||||
cusparseDnVecDescr_t vecp = NULL;
|
||||
|
|
@ -206,7 +204,7 @@ int main(int argc, char **argv) {
|
|||
size_t bufferSize = 0;
|
||||
checkCudaErrors(cusparseSpMV_bufferSize(
|
||||
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecx,
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &bufferSize));
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_SPMV_ALG_DEFAULT, &bufferSize));
|
||||
void *buffer = NULL;
|
||||
checkCudaErrors(cudaMalloc(&buffer, bufferSize));
|
||||
|
||||
|
|
@ -234,9 +232,9 @@ int main(int argc, char **argv) {
|
|||
beta = 0.0;
|
||||
|
||||
checkCudaErrors(cusparseSetStream(cusparseHandle, stream1));
|
||||
checkCudaErrors(cusparseSpMV(
|
||||
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecx,
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&alpha, matA, vecx, &beta, vecAx, CUDA_R_32F,
|
||||
CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
checkCudaErrors(cublasSetStream(cublasHandle, stream1));
|
||||
checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpham1, d_Ax, 1, d_r, 1));
|
||||
|
|
@ -248,9 +246,9 @@ int main(int argc, char **argv) {
|
|||
k = 1;
|
||||
// First Iteration when k=1 starts
|
||||
checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_p, 1));
|
||||
checkCudaErrors(cusparseSpMV(
|
||||
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecp,
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&alpha, matA, vecp, &beta, vecAx, CUDA_R_32F,
|
||||
CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
||||
|
|
@ -290,9 +288,9 @@ int main(int argc, char **argv) {
|
|||
|
||||
checkCudaErrors(
|
||||
cusparseSetPointerMode(cusparseHandle, CUSPARSE_POINTER_MODE_HOST));
|
||||
checkCudaErrors(cusparseSpMV(
|
||||
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecp,
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&alpha, matA, vecp, &beta, vecAx, CUDA_R_32F,
|
||||
CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
checkCudaErrors(cudaMemsetAsync(d_dot, 0, sizeof(float), stream1));
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
|
@ -336,7 +334,7 @@ int main(int argc, char **argv) {
|
|||
|
||||
checkCudaErrors(cusparseSpMV(
|
||||
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &alpha, matA, vecp,
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_MV_ALG_DEFAULT, &buffer));
|
||||
&beta, vecAx, CUDA_R_32F, CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
cublasSetPointerMode(cublasHandle, CUBLAS_POINTER_MODE_DEVICE);
|
||||
checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, d_dot));
|
||||
|
|
@ -395,23 +393,31 @@ int main(int argc, char **argv) {
|
|||
cusparseDestroy(cusparseHandle);
|
||||
cublasDestroy(cublasHandle);
|
||||
|
||||
if (matA ) { checkCudaErrors(cusparseDestroySpMat(matA)); }
|
||||
if (vecx ) { checkCudaErrors(cusparseDestroyDnVec(vecx)); }
|
||||
if (vecAx ) { checkCudaErrors(cusparseDestroyDnVec(vecAx)); }
|
||||
if (vecp ) { checkCudaErrors(cusparseDestroyDnVec(vecp)); }
|
||||
if (matA) {
|
||||
checkCudaErrors(cusparseDestroySpMat(matA));
|
||||
}
|
||||
if (vecx) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecx));
|
||||
}
|
||||
if (vecAx) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecAx));
|
||||
}
|
||||
if (vecp) {
|
||||
checkCudaErrors(cusparseDestroyDnVec(vecp));
|
||||
}
|
||||
|
||||
free(I);
|
||||
free(J);
|
||||
free(val);
|
||||
free(x);
|
||||
checkCudaErrors(cudaFreeHost(I));
|
||||
checkCudaErrors(cudaFreeHost(J));
|
||||
checkCudaErrors(cudaFreeHost(val));
|
||||
checkCudaErrors(cudaFreeHost(x));
|
||||
free(rhs);
|
||||
cudaFree(d_col);
|
||||
cudaFree(d_row);
|
||||
cudaFree(d_val);
|
||||
cudaFree(d_x);
|
||||
cudaFree(d_r);
|
||||
cudaFree(d_p);
|
||||
cudaFree(d_Ax);
|
||||
checkCudaErrors(cudaFree(d_col));
|
||||
checkCudaErrors(cudaFree(d_row));
|
||||
checkCudaErrors(cudaFree(d_val));
|
||||
checkCudaErrors(cudaFree(d_x));
|
||||
checkCudaErrors(cudaFree(d_r));
|
||||
checkCudaErrors(cudaFree(d_p));
|
||||
checkCudaErrors(cudaFree(d_Ax));
|
||||
|
||||
printf("Test Summary: Error amount = %f\n", err);
|
||||
exit((k <= max_iter) ? 0 : 1);
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ x86_64, ppc64le, aarch64
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cudaMemAdvise, cudaMemPrefetchAsync, cudaLaunchCooperativeKernelMultiDevice, cud
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -223,7 +223,9 @@ __device__ void gpuDotProduct(float *vecA, float *vecB, int size,
|
|||
cg::sync(cta);
|
||||
|
||||
if (tile32.meta_group_rank() == 0) {
|
||||
temp_sum = tile32.thread_rank() < tile32.meta_group_size() ? tmp[tile32.thread_rank()] : 0.0;
|
||||
temp_sum = tile32.thread_rank() < tile32.meta_group_size()
|
||||
? tmp[tile32.thread_rank()]
|
||||
: 0.0;
|
||||
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
|
||||
|
||||
if (tile32.thread_rank() == 0) {
|
||||
|
|
@ -239,7 +241,8 @@ __device__ void gpuCopyVector(float *srcA, float *destB, int size,
|
|||
}
|
||||
}
|
||||
|
||||
__device__ void gpuScaleVectorAndSaxpy(float *x, float *y, float a, float scale, int size,
|
||||
__device__ void gpuScaleVectorAndSaxpy(float *x, float *y, float a, float scale,
|
||||
int size,
|
||||
const cg::multi_grid_group &multi_grid) {
|
||||
for (int i = multi_grid.thread_rank(); i < size; i += multi_grid.size()) {
|
||||
y[i] = a * x[i] + scale * y[i];
|
||||
|
|
@ -360,7 +363,8 @@ std::multimap<std::pair<int, int>, int> getIdenticalGPUs() {
|
|||
// Filter unsupported devices
|
||||
if (deviceProp.cooperativeMultiDeviceLaunch &&
|
||||
deviceProp.concurrentManagedAccess) {
|
||||
identicalGpus.emplace(std::make_pair(deviceProp.major, deviceProp.minor), i);
|
||||
identicalGpus.emplace(std::make_pair(deviceProp.major, deviceProp.minor),
|
||||
i);
|
||||
}
|
||||
printf("GPU Device %d: \"%s\" with compute capability %d.%d\n", i,
|
||||
deviceProp.name, deviceProp.major, deviceProp.minor);
|
||||
|
|
@ -387,15 +391,17 @@ int main(int argc, char **argv) {
|
|||
|
||||
auto bestFit = std::make_pair(it, it);
|
||||
// use std::distance to find the largest number of GPUs amongst architectures
|
||||
auto distance = [](decltype(bestFit) p){return std::distance(p.first, p.second);};
|
||||
auto distance = [](decltype(bestFit) p) {
|
||||
return std::distance(p.first, p.second);
|
||||
};
|
||||
|
||||
// Read each unique key/pair element in order
|
||||
for (; it != end; it = gpusByArch.upper_bound(it->first)) {
|
||||
// first and second are iterators bounded within the architecture group
|
||||
auto testFit = gpusByArch.equal_range(it->first);
|
||||
// Always use devices with highest architecture version or whichever has the most devices available
|
||||
if (distance(bestFit) <= distance(testFit))
|
||||
bestFit = testFit;
|
||||
// Always use devices with highest architecture version or whichever has the
|
||||
// most devices available
|
||||
if (distance(bestFit) <= distance(testFit)) bestFit = testFit;
|
||||
}
|
||||
|
||||
if (distance(bestFit) < kNumGpusRequired) {
|
||||
|
|
@ -408,33 +414,35 @@ int main(int argc, char **argv) {
|
|||
|
||||
std::set<int> bestFitDeviceIds;
|
||||
|
||||
// check & select peer-to-peer access capable GPU devices as enabling p2p access between participating
|
||||
// check & select peer-to-peer access capable GPU devices as enabling p2p
|
||||
// access between participating
|
||||
// GPUs gives better performance for multi_grid sync.
|
||||
for (auto itr = bestFit.first; itr != bestFit.second; itr++) {
|
||||
int deviceId = itr->second;
|
||||
checkCudaErrors(cudaSetDevice(deviceId));
|
||||
|
||||
std::for_each(itr, bestFit.second, [&deviceId, &bestFitDeviceIds](decltype(*itr) mapPair) {
|
||||
if (deviceId != mapPair.second)
|
||||
{
|
||||
std::for_each(itr, bestFit.second, [&deviceId, &bestFitDeviceIds,
|
||||
&kNumGpusRequired](
|
||||
decltype(*itr) mapPair) {
|
||||
if (deviceId != mapPair.second) {
|
||||
int access = 0;
|
||||
checkCudaErrors(cudaDeviceCanAccessPeer(&access, deviceId, mapPair.second));
|
||||
printf("Device=%d %s Access Peer Device=%d\n", deviceId, access ? "CAN" : "CANNOT", mapPair.second);
|
||||
checkCudaErrors(
|
||||
cudaDeviceCanAccessPeer(&access, deviceId, mapPair.second));
|
||||
printf("Device=%d %s Access Peer Device=%d\n", deviceId,
|
||||
access ? "CAN" : "CANNOT", mapPair.second);
|
||||
if (access && bestFitDeviceIds.size() < kNumGpusRequired) {
|
||||
bestFitDeviceIds.emplace(deviceId);
|
||||
bestFitDeviceIds.emplace(mapPair.second);
|
||||
}
|
||||
else {
|
||||
} else {
|
||||
printf("Ignoring device %i (max devices exceeded)\n", mapPair.second);
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
if (bestFitDeviceIds.size() >= kNumGpusRequired)
|
||||
{
|
||||
if (bestFitDeviceIds.size() >= kNumGpusRequired) {
|
||||
printf("Selected p2p capable devices - ");
|
||||
for (auto devicesItr = bestFitDeviceIds.begin(); devicesItr != bestFitDeviceIds.end(); devicesItr++)
|
||||
{
|
||||
for (auto devicesItr = bestFitDeviceIds.begin();
|
||||
devicesItr != bestFitDeviceIds.end(); devicesItr++) {
|
||||
printf("deviceId = %d ", *devicesItr);
|
||||
}
|
||||
printf("\n");
|
||||
|
|
@ -442,33 +450,34 @@ int main(int argc, char **argv) {
|
|||
}
|
||||
}
|
||||
|
||||
// if bestFitDeviceIds.size() == 0 it means the GPUs in system are not p2p capable,
|
||||
// if bestFitDeviceIds.size() == 0 it means the GPUs in system are not p2p
|
||||
// capable,
|
||||
// hence we add it without p2p capability check.
|
||||
if (!bestFitDeviceIds.size())
|
||||
{
|
||||
printf("Devices involved are not p2p capable.. selecting %zu of them\n", kNumGpusRequired);
|
||||
std::for_each(bestFit.first, bestFit.second, [&bestFitDeviceIds](decltype(*bestFit.first) mapPair) {
|
||||
if (!bestFitDeviceIds.size()) {
|
||||
printf("Devices involved are not p2p capable.. selecting %zu of them\n",
|
||||
kNumGpusRequired);
|
||||
std::for_each(bestFit.first, bestFit.second,
|
||||
[&bestFitDeviceIds,
|
||||
&kNumGpusRequired](decltype(*bestFit.first) mapPair) {
|
||||
if (bestFitDeviceIds.size() < kNumGpusRequired) {
|
||||
bestFitDeviceIds.emplace(mapPair.second);
|
||||
}
|
||||
else {
|
||||
printf("Ignoring device %i (max devices exceeded)\n", mapPair.second);
|
||||
} else {
|
||||
printf("Ignoring device %i (max devices exceeded)\n",
|
||||
mapPair.second);
|
||||
}
|
||||
// Insert the sequence into the deviceIds set
|
||||
});
|
||||
}
|
||||
else
|
||||
{
|
||||
// perform cudaDeviceEnablePeerAccess in both directions for all participating devices
|
||||
// of a cudaLaunchCooperativeKernelMultiDevice call this gives better performance for multi_grid sync.
|
||||
for (auto p1_itr = bestFitDeviceIds.begin(); p1_itr != bestFitDeviceIds.end(); p1_itr++)
|
||||
{
|
||||
} else {
|
||||
// perform cudaDeviceEnablePeerAccess in both directions for all
|
||||
// participating devices of a cudaLaunchCooperativeKernelMultiDevice call
|
||||
// this gives better performance for multi_grid sync.
|
||||
for (auto p1_itr = bestFitDeviceIds.begin();
|
||||
p1_itr != bestFitDeviceIds.end(); p1_itr++) {
|
||||
checkCudaErrors(cudaSetDevice(*p1_itr));
|
||||
for (auto p2_itr = bestFitDeviceIds.begin(); p2_itr != bestFitDeviceIds.end(); p2_itr++)
|
||||
{
|
||||
if (*p1_itr != *p2_itr)
|
||||
{
|
||||
checkCudaErrors(cudaDeviceEnablePeerAccess(*p2_itr, 0 ));
|
||||
for (auto p2_itr = bestFitDeviceIds.begin();
|
||||
p2_itr != bestFitDeviceIds.end(); p2_itr++) {
|
||||
if (*p1_itr != *p2_itr) {
|
||||
checkCudaErrors(cudaDeviceEnablePeerAccess(*p2_itr, 0));
|
||||
checkCudaErrors(cudaSetDevice(*p1_itr));
|
||||
}
|
||||
}
|
||||
|
|
@ -518,7 +527,7 @@ int main(int argc, char **argv) {
|
|||
std::cout << "\nRunning on GPUs = " << kNumGpusRequired << std::endl;
|
||||
cudaStream_t nStreams[kNumGpusRequired];
|
||||
|
||||
int sMemSize = sizeof(double) * ((THREADS_PER_BLOCK/32) + 1);
|
||||
int sMemSize = sizeof(double) * ((THREADS_PER_BLOCK / 32) + 1);
|
||||
int numBlocksPerSm = INT_MAX;
|
||||
int numThreads = THREADS_PER_BLOCK;
|
||||
int numSms = INT_MAX;
|
||||
|
|
@ -530,16 +539,15 @@ int main(int argc, char **argv) {
|
|||
checkCudaErrors(cudaSetDevice(*deviceId));
|
||||
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, *deviceId));
|
||||
|
||||
int numBlocksPerSm_current=0;
|
||||
int numBlocksPerSm_current = 0;
|
||||
checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
|
||||
&numBlocksPerSm_current, multiGpuConjugateGradient, numThreads, sMemSize));
|
||||
&numBlocksPerSm_current, multiGpuConjugateGradient, numThreads,
|
||||
sMemSize));
|
||||
|
||||
if (numBlocksPerSm > numBlocksPerSm_current)
|
||||
{
|
||||
if (numBlocksPerSm > numBlocksPerSm_current) {
|
||||
numBlocksPerSm = numBlocksPerSm_current;
|
||||
}
|
||||
if (numSms > deviceProp.multiProcessorCount)
|
||||
{
|
||||
if (numSms > deviceProp.multiProcessorCount) {
|
||||
numSms = deviceProp.multiProcessorCount;
|
||||
}
|
||||
deviceId++;
|
||||
|
|
@ -554,7 +562,7 @@ int main(int argc, char **argv) {
|
|||
|
||||
int device_count = 0;
|
||||
int totalThreadsPerGPU = numSms * numBlocksPerSm * THREADS_PER_BLOCK;
|
||||
deviceId = bestFitDeviceIds.begin();;
|
||||
deviceId = bestFitDeviceIds.begin();
|
||||
while (deviceId != bestFitDeviceIds.end()) {
|
||||
checkCudaErrors(cudaSetDevice(*deviceId));
|
||||
checkCudaErrors(cudaStreamCreate(&nStreams[device_count]));
|
||||
|
|
@ -621,14 +629,15 @@ int main(int argc, char **argv) {
|
|||
|
||||
printf("Total threads per GPU = %d numBlocksPerSm = %d\n",
|
||||
numSms * numBlocksPerSm * THREADS_PER_BLOCK, numBlocksPerSm);
|
||||
dim3 dimGrid(numSms * numBlocksPerSm, 1, 1), dimBlock(THREADS_PER_BLOCK, 1, 1);
|
||||
dim3 dimGrid(numSms * numBlocksPerSm, 1, 1),
|
||||
dimBlock(THREADS_PER_BLOCK, 1, 1);
|
||||
void *kernelArgs[] = {
|
||||
(void *)&I, (void *)&J, (void *)&val, (void *)&x,
|
||||
(void *)&Ax, (void *)&p, (void *)&r, (void *)&dot_result,
|
||||
(void *)&nz, (void *)&N, (void *)&tol,
|
||||
};
|
||||
cudaLaunchParams *launchParamsList = (cudaLaunchParams *)malloc(
|
||||
sizeof(cudaLaunchParams) * kNumGpusRequired);
|
||||
cudaLaunchParams *launchParamsList =
|
||||
(cudaLaunchParams *)malloc(sizeof(cudaLaunchParams) * kNumGpusRequired);
|
||||
for (int i = 0; i < kNumGpusRequired; i++) {
|
||||
launchParamsList[i].func = (void *)multiGpuConjugateGradient;
|
||||
launchParamsList[i].gridDim = dimGrid;
|
||||
|
|
@ -645,12 +654,11 @@ int main(int argc, char **argv) {
|
|||
cudaCooperativeLaunchMultiDeviceNoPreSync |
|
||||
cudaCooperativeLaunchMultiDeviceNoPostSync));
|
||||
|
||||
checkCudaErrors(
|
||||
cudaMemPrefetchAsync(x, sizeof(float) * N, cudaCpuDeviceId));
|
||||
checkCudaErrors(cudaMemPrefetchAsync(x, sizeof(float) * N, cudaCpuDeviceId));
|
||||
checkCudaErrors(
|
||||
cudaMemPrefetchAsync(dot_result, sizeof(double), cudaCpuDeviceId));
|
||||
|
||||
deviceId = bestFitDeviceIds.begin();;
|
||||
deviceId = bestFitDeviceIds.begin();
|
||||
device_count = 0;
|
||||
while (deviceId != bestFitDeviceIds.end()) {
|
||||
checkCudaErrors(cudaSetDevice(*deviceId));
|
||||
|
|
@ -658,7 +666,7 @@ int main(int argc, char **argv) {
|
|||
deviceId++;
|
||||
}
|
||||
|
||||
r1 = *dot_result;
|
||||
r1 = (float)*dot_result;
|
||||
|
||||
printf("GPU Final, residual = %e \n ", sqrt(r1));
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -109,6 +109,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -105,6 +105,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -271,6 +271,12 @@ ifeq ($(TARGET_ARCH),armv7l)
|
|||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
# This sample is not supported on QNX
|
||||
ifeq ($(TARGET_OS),qnx)
|
||||
$(info >>> WARNING - cuSolverDn_LinearSolver is not supported on QNX - waiving sample <<<)
|
||||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
ifeq ($(TARGET_OS),linux)
|
||||
ALL_CCFLAGS += -Xcompiler \"-Wl,--no-as-needed\"
|
||||
endif
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ x86_64, ppc64le, aarch64
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -110,6 +110,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -106,6 +106,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -265,6 +265,12 @@ ALL_CCFLAGS += $(addprefix -Xcompiler ,$(EXTRA_CCFLAGS))
|
|||
|
||||
SAMPLE_ENABLED := 1
|
||||
|
||||
# This sample is not supported on QNX
|
||||
ifeq ($(TARGET_OS),qnx)
|
||||
$(info >>> WARNING - cuSolverSp_LinearSolver is not supported on QNX - waiving sample <<<)
|
||||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
ifeq ($(TARGET_OS),linux)
|
||||
ALL_CCFLAGS += -Xcompiler \"-Wl,--no-as-needed\"
|
||||
endif
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ x86_64, ppc64le, armv7l
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -495,13 +495,13 @@ int main(int argc, char *argv[]) {
|
|||
size_t bufferSize = 0;
|
||||
checkCudaErrors(cusparseSpMV_bufferSize(
|
||||
cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, &minus_one, matA, vecx,
|
||||
&one, vecAx, CUDA_R_64F, CUSPARSE_MV_ALG_DEFAULT, &bufferSize));
|
||||
&one, vecAx, CUDA_R_64F, CUSPARSE_SPMV_ALG_DEFAULT, &bufferSize));
|
||||
void *buffer = NULL;
|
||||
checkCudaErrors(cudaMalloc(&buffer, bufferSize));
|
||||
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&minus_one, matA, vecx, &one, vecAx, CUDA_R_64F,
|
||||
CUSPARSE_MV_ALG_DEFAULT, &buffer));
|
||||
CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(h_r, d_r, sizeof(double) * rowsA,
|
||||
cudaMemcpyDeviceToHost, stream));
|
||||
|
|
@ -559,7 +559,7 @@ int main(int argc, char *argv[]) {
|
|||
|
||||
checkCudaErrors(cusparseSpMV(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
|
||||
&minus_one, matA, vecx, &one, vecAx, CUDA_R_64F,
|
||||
CUSPARSE_MV_ALG_DEFAULT, &buffer));
|
||||
CUSPARSE_SPMV_ALG_DEFAULT, buffer));
|
||||
|
||||
checkCudaErrors(cudaMemcpyAsync(h_x, d_x, sizeof(double) * colsA,
|
||||
cudaMemcpyDeviceToHost, stream));
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -110,6 +110,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -106,6 +106,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cudaMalloc, cudaFree
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -109,6 +109,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -105,6 +105,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -279,6 +279,12 @@ ifeq ($(TARGET_ARCH),armv7l)
|
|||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
# This sample is not supported on QNX
|
||||
ifeq ($(TARGET_OS),qnx)
|
||||
$(info >>> WARNING - cudaNvSci is not supported on QNX - waiving sample <<<)
|
||||
SAMPLE_ENABLED := 0
|
||||
endif
|
||||
|
||||
ALL_LDFLAGS :=
|
||||
ALL_LDFLAGS += $(ALL_CCFLAGS)
|
||||
ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cudaImportExternalMemory, cudaExternalMemoryGetMappedBuffer, cudaExternalMemoryG
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cudaImportExternalMemory, cudaExternalMemoryGetMappedBuffer, cudaExternalMemoryG
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cudaMalloc, cudaFree, cudaMemcpy
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -109,6 +109,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -105,6 +105,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ cudaMallocManaged, cudaDeviceSynchronize, cudaFuncSetAttribute, cudaEventCreate,
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cudaSetDevice, cudaGetDeviceCount, cudaGetDeviceProperties, cudaDriverGetVersion
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ int main(int argc, char **argv) {
|
|||
#endif
|
||||
printf("%s", msg);
|
||||
|
||||
printf(" (%2d) Multiprocessors, (%3d) CUDA Cores/MP: %d CUDA Cores\n",
|
||||
printf(" (%03d) Multiprocessors, (%03d) CUDA Cores/MP: %d CUDA Cores\n",
|
||||
deviceProp.multiProcessorCount,
|
||||
_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor),
|
||||
_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) *
|
||||
|
|
@ -250,8 +250,7 @@ int main(int argc, char **argv) {
|
|||
"device)",
|
||||
"Exclusive Process (many threads in one process is able to use "
|
||||
"::cudaSetDevice() with this device)",
|
||||
"Unknown",
|
||||
NULL};
|
||||
"Unknown", NULL};
|
||||
printf(" Compute Mode:\n");
|
||||
printf(" < %s >\n", sComputeMode[deviceProp.computeMode]);
|
||||
}
|
||||
|
|
@ -307,7 +306,8 @@ int main(int argc, char **argv) {
|
|||
// driver version
|
||||
sProfileString += ", CUDA Driver Version = ";
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
sprintf_s(cTemp, 10, "%d.%d", driverVersion/1000, (driverVersion%100)/10);
|
||||
sprintf_s(cTemp, 10, "%d.%d", driverVersion / 1000,
|
||||
(driverVersion % 100) / 10);
|
||||
#else
|
||||
snprintf(cTemp, sizeof(cTemp), "%d.%d", driverVersion / 1000,
|
||||
(driverVersion % 100) / 10);
|
||||
|
|
@ -317,7 +317,8 @@ int main(int argc, char **argv) {
|
|||
// Runtime version
|
||||
sProfileString += ", CUDA Runtime Version = ";
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
sprintf_s(cTemp, 10, "%d.%d", runtimeVersion/1000, (runtimeVersion%100)/10);
|
||||
sprintf_s(cTemp, 10, "%d.%d", runtimeVersion / 1000,
|
||||
(runtimeVersion % 100) / 10);
|
||||
#else
|
||||
snprintf(cTemp, sizeof(cTemp), "%d.%d", runtimeVersion / 1000,
|
||||
(runtimeVersion % 100) / 10);
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cudaMallocManaged, cudaDeviceSynchronize, cudaFuncSetAttribute, cudaEventCreate,
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cudaEventCreate, cudaEventRecord, cudaEventQuery, cudaEventDestroy, cudaEventEla
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -28,12 +28,14 @@
|
|||
/**
|
||||
* Matrix multiplication: C = A * B.
|
||||
*
|
||||
* This sample demonstrates implements matrix multiplication which makes use of shared memory
|
||||
* to ensure data reuse, the matrix multiplication is done using tiling approach.
|
||||
* With compute capability 8.0 or higher the CUDA kernels involved uses asynchronously copy data
|
||||
* from global to shared memory; a.k.a., async-copy.
|
||||
* This sample has been written for clarity of exposition to illustrate various CUDA programming
|
||||
* principles, not with the goal of providing the most performant generic kernel for matrix multiplication.
|
||||
* This sample demonstrates implements matrix multiplication which makes use of
|
||||
* shared memory to ensure data reuse, the matrix multiplication is done using
|
||||
* tiling approach.
|
||||
* With compute capability 8.0 or higher the CUDA kernels involved uses
|
||||
* asynchronously copy data from global to shared memory; a.k.a., async-copy.
|
||||
* This sample has been written for clarity of exposition to illustrate various
|
||||
* CUDA programming principles, not with the goal of providing the most
|
||||
* performant generic kernel for matrix multiplication.
|
||||
*/
|
||||
|
||||
// System includes
|
||||
|
|
@ -55,8 +57,7 @@ namespace cg = cooperative_groups;
|
|||
#include <helper_functions.h>
|
||||
#include <helper_cuda.h>
|
||||
|
||||
enum kernels
|
||||
{
|
||||
enum kernels {
|
||||
AsyncCopyMultiStageLargeChunk = 0,
|
||||
AsyncCopyLargeChunk = 1,
|
||||
AsyncCopyLargeChunkAWBarrier = 2,
|
||||
|
|
@ -67,17 +68,22 @@ enum kernels
|
|||
NaiveLargeChunk = 7
|
||||
};
|
||||
|
||||
const char* kernelNames[] = {"AsyncCopyMultiStageLargeChunk", "AsyncCopyLargeChunk",
|
||||
"AsyncCopyLargeChunkAWBarrier", "AsyncCopyMultiStageSharedState",
|
||||
"AsyncCopyMultiStage", "AsyncCopySingleStage", "Naive", "NaiveLargeChunk"};
|
||||
const char *kernelNames[] = {"AsyncCopyMultiStageLargeChunk",
|
||||
"AsyncCopyLargeChunk",
|
||||
"AsyncCopyLargeChunkAWBarrier",
|
||||
"AsyncCopyMultiStageSharedState",
|
||||
"AsyncCopyMultiStage",
|
||||
"AsyncCopySingleStage",
|
||||
"Naive",
|
||||
"NaiveLargeChunk"};
|
||||
|
||||
constexpr int blockSize = 16;
|
||||
|
||||
// Multi Stage memcpy_async pipeline with large chunk copy
|
||||
template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStageLargeChunk(float* __restrict__ C,
|
||||
const float* __restrict__ A,
|
||||
const float* __restrict__ B, int wA,
|
||||
int wB) {
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void MatrixMulAsyncCopyMultiStageLargeChunk(
|
||||
float *__restrict__ C, const float *__restrict__ A,
|
||||
const float *__restrict__ B, int wA, int wB) {
|
||||
// Requires BLOCK_SIZE % 4 == 0
|
||||
|
||||
// Multi-stage pipeline version
|
||||
|
|
@ -85,16 +91,18 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStageLargeChunk
|
|||
|
||||
// Declaration of the shared memory array As used to
|
||||
// store the sub-matrix of A for each stage
|
||||
__shared__ alignas(alignof(float4)) float As[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
|
||||
__shared__ alignas(
|
||||
alignof(float4)) float As[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
|
||||
|
||||
// Declaration of the shared memory array Bs used to
|
||||
// store the sub-matrix of B for each stage
|
||||
__shared__ alignas(alignof(float4)) float Bs[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
|
||||
__shared__ alignas(
|
||||
alignof(float4)) float Bs[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
|
||||
|
||||
float Csub = 0.0;
|
||||
|
||||
// Index of the first sub-matrix of A processed by the block
|
||||
const int aBegin = wA * (BLOCK_SIZE) * blockIdx.y;
|
||||
const int aBegin = wA * (BLOCK_SIZE)*blockIdx.y;
|
||||
|
||||
// Index of the last sub-matrix of A processed by the block
|
||||
const int aEnd = aBegin + wA - 1;
|
||||
|
|
@ -115,18 +123,21 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStageLargeChunk
|
|||
|
||||
// Loop over all the sub-matrices of A and B
|
||||
// required to compute the block sub-matrix
|
||||
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin, iStage = 0; a <= aEnd; a += aStep, b += bStep, ++i ) {
|
||||
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin,
|
||||
iStage = 0;
|
||||
a <= aEnd; a += aStep, b += bStep, ++i) {
|
||||
// Load the matrices from device memory to shared memory; each thread loads
|
||||
// one element of each matrix
|
||||
for ( ; aStage <= a + aStep * maxPipelineStages ; aStage += aStep, bStage += bStep, ++iStage )
|
||||
{
|
||||
for (; aStage <= a + aStep * maxPipelineStages;
|
||||
aStage += aStep, bStage += bStep, ++iStage) {
|
||||
pipe.producer_acquire();
|
||||
if ( aStage <= aEnd && t4x < BLOCK_SIZE )
|
||||
{
|
||||
if (aStage <= aEnd && t4x < BLOCK_SIZE) {
|
||||
// Rotating buffer
|
||||
const int j = iStage % maxPipelineStages;
|
||||
cuda::memcpy_async(&As[j][threadIdx.y][t4x], &A[aStage + wA * threadIdx.y + t4x], shape4, pipe);
|
||||
cuda::memcpy_async(&Bs[j][threadIdx.y][t4x], &B[aStage + wA * threadIdx.y + t4x], shape4, pipe);
|
||||
cuda::memcpy_async(&As[j][threadIdx.y][t4x],
|
||||
&A[aStage + wA * threadIdx.y + t4x], shape4, pipe);
|
||||
cuda::memcpy_async(&Bs[j][threadIdx.y][t4x],
|
||||
&B[aStage + wA * threadIdx.y + t4x], shape4, pipe);
|
||||
}
|
||||
pipe.producer_commit();
|
||||
}
|
||||
|
|
@ -138,10 +149,10 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStageLargeChunk
|
|||
// Rotating buffer
|
||||
const int j = i % maxPipelineStages;
|
||||
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE; ++k) {
|
||||
Csub += As[j][threadIdx.y][k] * Bs[j][k][threadIdx.x];
|
||||
}
|
||||
|
|
@ -157,12 +168,12 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStageLargeChunk
|
|||
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
|
||||
}
|
||||
|
||||
|
||||
// Single Stage memcpy_async pipeline with Large copy chunk (float4)
|
||||
template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunk(float* __restrict__ C,
|
||||
const float* __restrict__ A,
|
||||
const float* __restrict__ B, int wA,
|
||||
int wB) {
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void MatrixMulAsyncCopyLargeChunk(float *__restrict__ C,
|
||||
const float *__restrict__ A,
|
||||
const float *__restrict__ B,
|
||||
int wA, int wB) {
|
||||
// Requires BLOCK_SIZE % 4 == 0
|
||||
|
||||
// Declaration of the shared memory array As used to
|
||||
|
|
@ -206,12 +217,13 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunk(float* __
|
|||
// Bs[ty][tx] = B[b + wB * ty + tx];
|
||||
|
||||
// Now, one fourth of the threads load four elements of each matrix
|
||||
if ( t4x < BLOCK_SIZE ) {
|
||||
|
||||
if (t4x < BLOCK_SIZE) {
|
||||
pipe.producer_acquire();
|
||||
|
||||
cuda::memcpy_async(&As[threadIdx.y][t4x], &A[a + wA * threadIdx.y + t4x], shape4, pipe);
|
||||
cuda::memcpy_async(&Bs[threadIdx.y][t4x], &B[a + wA * threadIdx.y + t4x], shape4, pipe);
|
||||
cuda::memcpy_async(&As[threadIdx.y][t4x], &A[a + wA * threadIdx.y + t4x],
|
||||
shape4, pipe);
|
||||
cuda::memcpy_async(&Bs[threadIdx.y][t4x], &B[a + wA * threadIdx.y + t4x],
|
||||
shape4, pipe);
|
||||
|
||||
pipe.producer_commit();
|
||||
pipe.consumer_wait();
|
||||
|
|
@ -220,9 +232,9 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunk(float* __
|
|||
// Synchronize to make sure the matrices are loaded
|
||||
__syncthreads();
|
||||
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE; ++k) {
|
||||
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
|
||||
|
|
@ -242,11 +254,12 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunk(float* __
|
|||
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
|
||||
}
|
||||
|
||||
// Single Stage memcpy_async pipeline with Large copy chunk (float4) using arrive-wait barrier
|
||||
template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunkAWBarrier(float* __restrict__ C,
|
||||
const float* __restrict__ A,
|
||||
const float* __restrict__ B, int wA,
|
||||
int wB) {
|
||||
// Single Stage memcpy_async pipeline with Large copy chunk (float4) using
|
||||
// arrive-wait barrier
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void MatrixMulAsyncCopyLargeChunkAWBarrier(
|
||||
float *__restrict__ C, const float *__restrict__ A,
|
||||
const float *__restrict__ B, int wA, int wB) {
|
||||
#if __CUDA_ARCH__ >= 700
|
||||
#pragma diag_suppress static_var_with_dynamic_init
|
||||
// Requires BLOCK_SIZE % 4 == 0
|
||||
|
|
@ -262,7 +275,7 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunkAWBarrier(
|
|||
__shared__ alignas(alignof(float4)) float Bs[BLOCK_SIZE][BLOCK_SIZE];
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
init(&bar, blockDim.x*blockDim.y);
|
||||
init(&bar, blockDim.x * blockDim.y);
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
|
|
@ -292,11 +305,13 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunkAWBarrier(
|
|||
// a subset of threads loads a contiguous chunk of elements.
|
||||
|
||||
// Now, one fourth of the threads load four elements of each matrix
|
||||
if ( t4x < BLOCK_SIZE ) {
|
||||
float4 * const A4s = reinterpret_cast<float4*>(& As[threadIdx.y][t4x]);
|
||||
float4 * const B4s = reinterpret_cast<float4*>(& Bs[threadIdx.y][t4x]);
|
||||
const float4 * const A4 = reinterpret_cast<const float4*>(& A[a + wA * threadIdx.y + t4x]);
|
||||
const float4 * const B4 = reinterpret_cast<const float4*>(& B[a + wA * threadIdx.y + t4x]);
|
||||
if (t4x < BLOCK_SIZE) {
|
||||
float4 *const A4s = reinterpret_cast<float4 *>(&As[threadIdx.y][t4x]);
|
||||
float4 *const B4s = reinterpret_cast<float4 *>(&Bs[threadIdx.y][t4x]);
|
||||
const float4 *const A4 =
|
||||
reinterpret_cast<const float4 *>(&A[a + wA * threadIdx.y + t4x]);
|
||||
const float4 *const B4 =
|
||||
reinterpret_cast<const float4 *>(&B[a + wA * threadIdx.y + t4x]);
|
||||
|
||||
cuda::memcpy_async(A4s, A4, sizeof(float4), bar);
|
||||
cuda::memcpy_async(B4s, B4, sizeof(float4), bar);
|
||||
|
|
@ -305,9 +320,9 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunkAWBarrier(
|
|||
// Synchronize to make sure the matrices are loaded
|
||||
bar.arrive_and_wait();
|
||||
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE; ++k) {
|
||||
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
|
||||
|
|
@ -327,10 +342,9 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyLargeChunkAWBarrier(
|
|||
}
|
||||
|
||||
// Single Stage memcpy_async pipeline with float copy
|
||||
template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopySingleStage(float *C, const float *A,
|
||||
const float *B, int wA,
|
||||
int wB) {
|
||||
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void MatrixMulAsyncCopySingleStage(float *C, const float *A,
|
||||
const float *B, int wA, int wB) {
|
||||
// Declaration of the shared memory array As used to
|
||||
// store the sub-matrix of A
|
||||
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
|
||||
|
|
@ -360,7 +374,6 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopySingleStage(float *C
|
|||
cuda::pipeline<cuda::thread_scope_thread> pipe = cuda::make_pipeline();
|
||||
const auto shape1 = cuda::aligned_size_t<alignof(float)>(sizeof(float));
|
||||
|
||||
|
||||
// Loop over all the sub-matrices of A and B
|
||||
// required to compute the block sub-matrix
|
||||
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
|
||||
|
|
@ -369,8 +382,10 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopySingleStage(float *C
|
|||
{
|
||||
pipe.producer_acquire();
|
||||
|
||||
cuda::memcpy_async(&As[threadIdx.y][threadIdx.x], &A[a + wA * threadIdx.y + threadIdx.x], shape1, pipe);
|
||||
cuda::memcpy_async(&Bs[threadIdx.y][threadIdx.x], &B[b + wB * threadIdx.y + threadIdx.x], shape1, pipe);
|
||||
cuda::memcpy_async(&As[threadIdx.y][threadIdx.x],
|
||||
&A[a + wA * threadIdx.y + threadIdx.x], shape1, pipe);
|
||||
cuda::memcpy_async(&Bs[threadIdx.y][threadIdx.x],
|
||||
&B[b + wB * threadIdx.y + threadIdx.x], shape1, pipe);
|
||||
|
||||
pipe.producer_commit();
|
||||
}
|
||||
|
|
@ -379,9 +394,9 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopySingleStage(float *C
|
|||
// Synchronize to make sure the matrices are loaded
|
||||
__syncthreads();
|
||||
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE; ++k) {
|
||||
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
|
||||
|
|
@ -399,11 +414,13 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopySingleStage(float *C
|
|||
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
|
||||
}
|
||||
|
||||
// Multi Stage memcpy_async thread_scope_thread pipeline with single-element async-copy
|
||||
template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStage(float* __restrict__ C,
|
||||
const float* __restrict__ A,
|
||||
const float* __restrict__ B, int wA,
|
||||
int wB) {
|
||||
// Multi Stage memcpy_async thread_scope_thread pipeline with single-element
|
||||
// async-copy
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void MatrixMulAsyncCopyMultiStage(float *__restrict__ C,
|
||||
const float *__restrict__ A,
|
||||
const float *__restrict__ B,
|
||||
int wA, int wB) {
|
||||
// Multi-stage pipeline version
|
||||
constexpr size_t maxPipelineStages = 4;
|
||||
|
||||
|
|
@ -437,21 +454,26 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStage(float* __
|
|||
|
||||
// Loop over all the sub-matrices of A and B
|
||||
// required to compute the block sub-matrix
|
||||
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin, iStage = 0; a <= aEnd; a += aStep, b += bStep, ++i ) {
|
||||
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin,
|
||||
iStage = 0;
|
||||
a <= aEnd; a += aStep, b += bStep, ++i) {
|
||||
// Load the matrices from device memory to shared memory; each thread loads
|
||||
// one element of each matrix
|
||||
|
||||
for ( ; aStage <= a + aStep * maxPipelineStages ; aStage += aStep, bStage += bStep, ++iStage )
|
||||
{
|
||||
if ( aStage <= aEnd )
|
||||
{
|
||||
for (; aStage <= a + aStep * maxPipelineStages;
|
||||
aStage += aStep, bStage += bStep, ++iStage) {
|
||||
if (aStage <= aEnd) {
|
||||
// Rotating buffer
|
||||
const int j = iStage % maxPipelineStages;
|
||||
|
||||
pipe.producer_acquire();
|
||||
|
||||
cuda::memcpy_async(&As[j][threadIdx.y][threadIdx.x], &A[aStage + wA * threadIdx.y + threadIdx.x], shape1, pipe);
|
||||
cuda::memcpy_async(&Bs[j][threadIdx.y][threadIdx.x], &B[bStage + wB * threadIdx.y + threadIdx.x], shape1, pipe);
|
||||
cuda::memcpy_async(&As[j][threadIdx.y][threadIdx.x],
|
||||
&A[aStage + wA * threadIdx.y + threadIdx.x], shape1,
|
||||
pipe);
|
||||
cuda::memcpy_async(&Bs[j][threadIdx.y][threadIdx.x],
|
||||
&B[bStage + wB * threadIdx.y + threadIdx.x], shape1,
|
||||
pipe);
|
||||
|
||||
pipe.producer_commit();
|
||||
}
|
||||
|
|
@ -484,11 +506,12 @@ template <int BLOCK_SIZE> __global__ void MatrixMulAsyncCopyMultiStage(float* __
|
|||
// Multi Stage shared state memcpy_async pipeline thread_scope_block
|
||||
// with parititioned producer & consumer, here we've 1 warp as producer
|
||||
// group which issues memcpy_async operations and rest all warps are part of
|
||||
// consumer group which perform gemm computation on the loaded matrices by producer.
|
||||
template <int BLOCK_SIZE_X> __global__ void MatrixMulAsyncCopyMultiStageSharedState(float* __restrict__ C,
|
||||
const float* __restrict__ A,
|
||||
const float* __restrict__ B, int wA,
|
||||
int wB) {
|
||||
// consumer group which perform gemm computation on the loaded matrices by
|
||||
// producer.
|
||||
template <int BLOCK_SIZE_X>
|
||||
__global__ void MatrixMulAsyncCopyMultiStageSharedState(
|
||||
float *__restrict__ C, const float *__restrict__ A,
|
||||
const float *__restrict__ B, int wA, int wB) {
|
||||
// Multi-stage pipeline version
|
||||
constexpr size_t maxPipelineStages = 4;
|
||||
|
||||
|
|
@ -520,7 +543,8 @@ template <int BLOCK_SIZE_X> __global__ void MatrixMulAsyncCopyMultiStageSharedSt
|
|||
auto cta = cg::this_thread_block();
|
||||
|
||||
const auto shape1 = cuda::aligned_size_t<alignof(float)>(sizeof(float));
|
||||
__shared__ cuda::pipeline_shared_state<cuda::thread_scope_block, maxPipelineStages> shared_state;
|
||||
__shared__ cuda::pipeline_shared_state<cuda::thread_scope_block,
|
||||
maxPipelineStages> shared_state;
|
||||
constexpr int consumer_row_count = BLOCK_SIZE_X;
|
||||
|
||||
const auto thread_role = (cta.thread_index().y < consumer_row_count)
|
||||
|
|
@ -530,37 +554,45 @@ template <int BLOCK_SIZE_X> __global__ void MatrixMulAsyncCopyMultiStageSharedSt
|
|||
|
||||
// Loop over all the sub-matrices of A and B
|
||||
// required to compute the block sub-matrix
|
||||
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin, iStage = 0;
|
||||
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin,
|
||||
iStage = 0;
|
||||
a <= aEnd; a += aStep, b += bStep, ++i) {
|
||||
if (threadIdx.y >= consumer_row_count) {
|
||||
// this is a whole producer warp because threadIdx.y >= 16 where 16 == consumer_row_count,
|
||||
// this is a whole producer warp because threadIdx.y >= 16 where 16 ==
|
||||
// consumer_row_count,
|
||||
// which loads the matrices from device memory to shared memory;
|
||||
for (; aStage <= a + aStep * maxPipelineStages; aStage += aStep, bStage += bStep, ++iStage) {
|
||||
for (; aStage <= a + aStep * maxPipelineStages;
|
||||
aStage += aStep, bStage += bStep, ++iStage) {
|
||||
if (aStage <= aEnd) {
|
||||
// Rotating buffer
|
||||
const int j = iStage % maxPipelineStages;
|
||||
const int strideRows = (blockDim.y - consumer_row_count);
|
||||
pipe.producer_acquire();
|
||||
for (int rowId = threadIdx.y - consumer_row_count; rowId < BLOCK_SIZE_X; rowId += strideRows) {
|
||||
for (int rowId = threadIdx.y - consumer_row_count;
|
||||
rowId < BLOCK_SIZE_X; rowId += strideRows) {
|
||||
cuda::memcpy_async(&As[j][rowId][threadIdx.x],
|
||||
&A[aStage + wA * rowId + threadIdx.x], shape1, pipe);
|
||||
&A[aStage + wA * rowId + threadIdx.x], shape1,
|
||||
pipe);
|
||||
cuda::memcpy_async(&Bs[j][rowId][threadIdx.x],
|
||||
&B[bStage + wB * rowId + threadIdx.x], shape1, pipe);
|
||||
&B[bStage + wB * rowId + threadIdx.x], shape1,
|
||||
pipe);
|
||||
}
|
||||
pipe.producer_commit();
|
||||
}
|
||||
}
|
||||
}
|
||||
else {
|
||||
// this is a whole set of consumer group because threadIdx.y < consumer_row_count where consumer_row_count == 16,
|
||||
// which computes gemm operation on matrices loaded in shared memory by producer warp.
|
||||
} else {
|
||||
// this is a whole set of consumer group because threadIdx.y <
|
||||
// consumer_row_count where consumer_row_count == 16,
|
||||
// which computes gemm operation on matrices loaded in shared memory by
|
||||
// producer warp.
|
||||
const int j = i % maxPipelineStages;
|
||||
// Synchronize consumer group to make sure the matrices are loaded by producer group.
|
||||
// Synchronize consumer group to make sure the matrices are loaded by
|
||||
// producer group.
|
||||
pipe.consumer_wait();
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE_X; ++k) {
|
||||
Csub += As[j][threadIdx.y][k] * Bs[j][k][threadIdx.x];
|
||||
}
|
||||
|
|
@ -570,8 +602,7 @@ template <int BLOCK_SIZE_X> __global__ void MatrixMulAsyncCopyMultiStageSharedSt
|
|||
|
||||
// Write the block sub-matrix to device memory;
|
||||
// each thread writes four element
|
||||
if (threadIdx.y < consumer_row_count)
|
||||
{
|
||||
if (threadIdx.y < consumer_row_count) {
|
||||
const int c = wB * BLOCK_SIZE_X * blockIdx.y + BLOCK_SIZE_X * blockIdx.x;
|
||||
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
|
||||
}
|
||||
|
|
@ -581,9 +612,8 @@ template <int BLOCK_SIZE_X> __global__ void MatrixMulAsyncCopyMultiStageSharedSt
|
|||
* Matrix multiplication (CUDA Kernel) on the device: C = A * B
|
||||
* wA is A's width and wB is B's width
|
||||
*/
|
||||
template <int BLOCK_SIZE> __global__ void MatrixMulNaive(float *C, float *A,
|
||||
float *B, int wA,
|
||||
int wB) {
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void MatrixMulNaive(float *C, float *A, float *B, int wA, int wB) {
|
||||
// Declaration of the shared memory array As used to
|
||||
// store the sub-matrix of A
|
||||
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
|
||||
|
|
@ -613,10 +643,7 @@ template <int BLOCK_SIZE> __global__ void MatrixMulNaive(float *C, float *A,
|
|||
|
||||
// Loop over all the sub-matrices of A and B
|
||||
// required to compute the block sub-matrix
|
||||
for (int a = aBegin, b = bBegin;
|
||||
a <= aEnd;
|
||||
a += aStep, b += bStep) {
|
||||
|
||||
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
|
||||
// Load the matrices from device memory
|
||||
// to shared memory; each thread loads
|
||||
// one element of each matrix
|
||||
|
|
@ -626,9 +653,9 @@ template <int BLOCK_SIZE> __global__ void MatrixMulNaive(float *C, float *A,
|
|||
// Synchronize to make sure the matrices are loaded
|
||||
__syncthreads();
|
||||
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE; ++k) {
|
||||
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
|
||||
|
|
@ -646,8 +673,8 @@ template <int BLOCK_SIZE> __global__ void MatrixMulNaive(float *C, float *A,
|
|||
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
|
||||
}
|
||||
|
||||
template <int BLOCK_SIZE> __global__ void MatrixMulNaiveLargeChunk(float *C, float *A,
|
||||
float *B, int wA,
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void MatrixMulNaiveLargeChunk(float *C, float *A, float *B, int wA,
|
||||
int wB) {
|
||||
// Declaration of the shared memory array As used to
|
||||
// store the sub-matrix of A
|
||||
|
|
@ -657,7 +684,7 @@ template <int BLOCK_SIZE> __global__ void MatrixMulNaiveLargeChunk(float *C, flo
|
|||
// store the sub-matrix of B
|
||||
__shared__ alignas(alignof(float4)) float Bs[BLOCK_SIZE][BLOCK_SIZE];
|
||||
|
||||
int t4x = threadIdx.x * 4 ;
|
||||
int t4x = threadIdx.x * 4;
|
||||
|
||||
// Index of the first sub-matrix of A processed by the block
|
||||
int aBegin = wA * BLOCK_SIZE * blockIdx.y;
|
||||
|
|
@ -680,29 +707,28 @@ template <int BLOCK_SIZE> __global__ void MatrixMulNaiveLargeChunk(float *C, flo
|
|||
|
||||
// Loop over all the sub-matrices of A and B
|
||||
// required to compute the block sub-matrix
|
||||
for (int a = aBegin, b = bBegin;
|
||||
a <= aEnd;
|
||||
a += aStep, b += bStep) {
|
||||
|
||||
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
|
||||
// Load the matrices from device memory
|
||||
// to shared memory;
|
||||
|
||||
// One fourth of the threads load four elements of each matrix
|
||||
if ( t4x < BLOCK_SIZE ) {
|
||||
float4 * const A4s = reinterpret_cast<float4*>(& As[threadIdx.y][t4x]);
|
||||
float4 * const B4s = reinterpret_cast<float4*>(& Bs[threadIdx.y][t4x]);
|
||||
const float4 * const A4 = reinterpret_cast<float4*>(& A[a + wA * threadIdx.y + t4x]);
|
||||
const float4 * const B4 = reinterpret_cast<float4*>(& B[a + wA * threadIdx.y + t4x]);
|
||||
*A4s = *A4 ;
|
||||
*B4s = *B4 ;
|
||||
if (t4x < BLOCK_SIZE) {
|
||||
float4 *const A4s = reinterpret_cast<float4 *>(&As[threadIdx.y][t4x]);
|
||||
float4 *const B4s = reinterpret_cast<float4 *>(&Bs[threadIdx.y][t4x]);
|
||||
const float4 *const A4 =
|
||||
reinterpret_cast<float4 *>(&A[a + wA * threadIdx.y + t4x]);
|
||||
const float4 *const B4 =
|
||||
reinterpret_cast<float4 *>(&B[a + wA * threadIdx.y + t4x]);
|
||||
*A4s = *A4;
|
||||
*B4s = *B4;
|
||||
}
|
||||
|
||||
// Synchronize to make sure the matrices are loaded
|
||||
__syncthreads();
|
||||
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
// Multiply the two matrices together;
|
||||
// each thread computes one element
|
||||
// of the block sub-matrix
|
||||
#pragma unroll
|
||||
for (int k = 0; k < BLOCK_SIZE; ++k) {
|
||||
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
|
||||
|
|
@ -720,7 +746,6 @@ template <int BLOCK_SIZE> __global__ void MatrixMulNaiveLargeChunk(float *C, flo
|
|||
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
|
||||
}
|
||||
|
||||
|
||||
void ConstantInit(float *data, int size, float val) {
|
||||
for (int i = 0; i < size; ++i) {
|
||||
data[i] = val;
|
||||
|
|
@ -730,18 +755,16 @@ void ConstantInit(float *data, int size, float val) {
|
|||
/**
|
||||
* Run matrix multiplication using CUDA
|
||||
*/
|
||||
int MatrixMultiply(int argc, char **argv,
|
||||
const dim3 &dimsA,
|
||||
const dim3 &dimsB,
|
||||
int MatrixMultiply(int argc, char **argv, const dim3 &dimsA, const dim3 &dimsB,
|
||||
kernels kernel_number) {
|
||||
// Allocate host memory for matrices A and B
|
||||
unsigned int size_A = dimsA.x * dimsA.y;
|
||||
unsigned int mem_size_A = sizeof(float) * size_A;
|
||||
float* h_A;
|
||||
float *h_A;
|
||||
checkCudaErrors(cudaMallocHost(&h_A, mem_size_A));
|
||||
unsigned int size_B = dimsB.x * dimsB.y;
|
||||
unsigned int mem_size_B = sizeof(float) * size_B;
|
||||
float* h_B;
|
||||
float *h_B;
|
||||
checkCudaErrors(cudaMallocHost(&h_B, mem_size_B));
|
||||
cudaStream_t stream;
|
||||
|
||||
|
|
@ -756,7 +779,7 @@ int MatrixMultiply(int argc, char **argv,
|
|||
// Allocate host matrix C
|
||||
dim3 dimsC(dimsB.x, dimsA.y, 1);
|
||||
unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float);
|
||||
float* h_C;
|
||||
float *h_C;
|
||||
checkCudaErrors(cudaMallocHost(&h_C, mem_size_C));
|
||||
|
||||
if (h_C == NULL) {
|
||||
|
|
@ -775,8 +798,10 @@ int MatrixMultiply(int argc, char **argv,
|
|||
checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
|
||||
|
||||
// copy host memory to device
|
||||
checkCudaErrors(cudaMemcpyAsync(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice, stream));
|
||||
checkCudaErrors(cudaMemcpyAsync(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice, stream));
|
||||
checkCudaErrors(
|
||||
cudaMemcpyAsync(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice, stream));
|
||||
checkCudaErrors(
|
||||
cudaMemcpyAsync(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice, stream));
|
||||
checkCudaErrors(cudaMemsetAsync(d_C, 0, mem_size_C, stream));
|
||||
|
||||
// Setup execution parameters
|
||||
|
|
@ -786,47 +811,57 @@ int MatrixMultiply(int argc, char **argv,
|
|||
// Here the block size is 16x18, where first 16 rows are consumer thread group
|
||||
// and last 2 rows (1 warp) is producer thread group
|
||||
dim3 threadsSharedStateKernel(blockSize, blockSize + 2, 1);
|
||||
dim3 gridSharedStateKernel(dimsB.x / threadsSharedStateKernel.x, dimsA.y / threadsSharedStateKernel.x);
|
||||
dim3 gridSharedStateKernel(dimsB.x / threadsSharedStateKernel.x,
|
||||
dimsA.y / threadsSharedStateKernel.x);
|
||||
|
||||
printf("Running kernel = %d - %s\n", kernel_number, kernelNames[kernel_number]);
|
||||
printf("Running kernel = %d - %s\n", kernel_number,
|
||||
kernelNames[kernel_number]);
|
||||
// Create and start timer
|
||||
printf("Computing result using CUDA Kernel...\n");
|
||||
|
||||
// Performs warmup operation using matrixMul CUDA kernel
|
||||
switch (kernel_number)
|
||||
{
|
||||
case AsyncCopyMultiStageLargeChunk :
|
||||
switch (kernel_number) {
|
||||
case AsyncCopyMultiStageLargeChunk:
|
||||
default:
|
||||
MatrixMulAsyncCopyMultiStageLargeChunk<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
MatrixMulAsyncCopyMultiStageLargeChunk<
|
||||
blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x,
|
||||
dimsB.x);
|
||||
break;
|
||||
case AsyncCopyLargeChunk :
|
||||
MatrixMulAsyncCopyLargeChunk<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyLargeChunk:
|
||||
MatrixMulAsyncCopyLargeChunk<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case AsyncCopyLargeChunkAWBarrier :
|
||||
MatrixMulAsyncCopyLargeChunkAWBarrier<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyLargeChunkAWBarrier:
|
||||
MatrixMulAsyncCopyLargeChunkAWBarrier<
|
||||
blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x,
|
||||
dimsB.x);
|
||||
break;
|
||||
case AsyncCopyMultiStageSharedState :
|
||||
MatrixMulAsyncCopyMultiStageSharedState<blockSize><<<gridSharedStateKernel, threadsSharedStateKernel, 0, stream>>>
|
||||
(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyMultiStageSharedState:
|
||||
MatrixMulAsyncCopyMultiStageSharedState<blockSize><<<
|
||||
gridSharedStateKernel, threadsSharedStateKernel, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case AsyncCopyMultiStage :
|
||||
MatrixMulAsyncCopyMultiStage<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyMultiStage:
|
||||
MatrixMulAsyncCopyMultiStage<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case AsyncCopySingleStage :
|
||||
MatrixMulAsyncCopySingleStage<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopySingleStage:
|
||||
MatrixMulAsyncCopySingleStage<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case Naive :
|
||||
MatrixMulNaive<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case Naive:
|
||||
MatrixMulNaive<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B,
|
||||
dimsA.x, dimsB.x);
|
||||
break;
|
||||
case NaiveLargeChunk:
|
||||
MatrixMulNaiveLargeChunk<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
MatrixMulNaiveLargeChunk<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
}
|
||||
|
||||
printf("done\n");
|
||||
checkCudaErrors(cudaStreamSynchronize(stream));
|
||||
|
||||
|
||||
// Execute the kernel
|
||||
int nIter = 100;
|
||||
|
||||
|
|
@ -834,33 +869,42 @@ int MatrixMultiply(int argc, char **argv,
|
|||
checkCudaErrors(cudaEventRecord(start, stream));
|
||||
|
||||
for (int j = 0; j < nIter; j++) {
|
||||
switch (kernel_number)
|
||||
{
|
||||
case AsyncCopyMultiStageLargeChunk :
|
||||
switch (kernel_number) {
|
||||
case AsyncCopyMultiStageLargeChunk:
|
||||
default:
|
||||
MatrixMulAsyncCopyMultiStageLargeChunk<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
MatrixMulAsyncCopyMultiStageLargeChunk<
|
||||
blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x,
|
||||
dimsB.x);
|
||||
break;
|
||||
case AsyncCopyLargeChunk :
|
||||
MatrixMulAsyncCopyLargeChunk<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyLargeChunk:
|
||||
MatrixMulAsyncCopyLargeChunk<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case AsyncCopyLargeChunkAWBarrier :
|
||||
MatrixMulAsyncCopyLargeChunkAWBarrier<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyLargeChunkAWBarrier:
|
||||
MatrixMulAsyncCopyLargeChunkAWBarrier<
|
||||
blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x,
|
||||
dimsB.x);
|
||||
break;
|
||||
case AsyncCopyMultiStageSharedState :
|
||||
MatrixMulAsyncCopyMultiStageSharedState<blockSize><<<gridSharedStateKernel, threadsSharedStateKernel, 0, stream>>>
|
||||
(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyMultiStageSharedState:
|
||||
MatrixMulAsyncCopyMultiStageSharedState<blockSize><<<
|
||||
gridSharedStateKernel, threadsSharedStateKernel, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case AsyncCopyMultiStage :
|
||||
MatrixMulAsyncCopyMultiStage<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopyMultiStage:
|
||||
MatrixMulAsyncCopyMultiStage<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case AsyncCopySingleStage :
|
||||
MatrixMulAsyncCopySingleStage<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case AsyncCopySingleStage:
|
||||
MatrixMulAsyncCopySingleStage<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case Naive :
|
||||
MatrixMulNaive<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
case Naive:
|
||||
MatrixMulNaive<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
case NaiveLargeChunk:
|
||||
MatrixMulNaiveLargeChunk<blockSize><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
MatrixMulNaiveLargeChunk<blockSize><<<grid, threads, 0, stream>>>(
|
||||
d_C, d_A, d_B, dimsA.x, dimsB.x);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
|
@ -879,18 +923,16 @@ int MatrixMultiply(int argc, char **argv,
|
|||
double flopsPerMatrixMul = 2.0 * static_cast<double>(dimsA.x) *
|
||||
static_cast<double>(dimsA.y) *
|
||||
static_cast<double>(dimsB.x);
|
||||
double gigaFlops = (flopsPerMatrixMul * 1.0e-9f) /
|
||||
(msecPerMatrixMul / 1000.0f);
|
||||
double gigaFlops =
|
||||
(flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f);
|
||||
printf(
|
||||
"Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops," \
|
||||
"Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops,"
|
||||
" WorkgroupSize= %u threads/block\n",
|
||||
gigaFlops,
|
||||
msecPerMatrixMul,
|
||||
flopsPerMatrixMul,
|
||||
threads.x * threads.y);
|
||||
gigaFlops, msecPerMatrixMul, flopsPerMatrixMul, threads.x * threads.y);
|
||||
|
||||
// Copy result from device to host
|
||||
checkCudaErrors(cudaMemcpyAsync(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost, stream));
|
||||
checkCudaErrors(
|
||||
cudaMemcpyAsync(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost, stream));
|
||||
checkCudaErrors(cudaStreamSynchronize(stream));
|
||||
|
||||
printf("Checking computed result for correctness: ");
|
||||
|
|
@ -907,8 +949,8 @@ int MatrixMultiply(int argc, char **argv,
|
|||
double rel_err = abs_err / abs_val / dot_length;
|
||||
|
||||
if (rel_err > eps) {
|
||||
printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n",
|
||||
i, h_C[i], dimsA.x * valB, eps);
|
||||
printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n", i,
|
||||
h_C[i], dimsA.x * valB, eps);
|
||||
correct = false;
|
||||
}
|
||||
}
|
||||
|
|
@ -924,7 +966,8 @@ int MatrixMultiply(int argc, char **argv,
|
|||
checkCudaErrors(cudaFree(d_C));
|
||||
checkCudaErrors(cudaEventDestroy(start));
|
||||
checkCudaErrors(cudaEventDestroy(stop));
|
||||
printf("\nNOTE: The CUDA Samples are not meant for performance "\
|
||||
printf(
|
||||
"\nNOTE: The CUDA Samples are not meant for performance "
|
||||
"measurements. Results may vary when GPU Boost is enabled.\n");
|
||||
|
||||
if (correct) {
|
||||
|
|
@ -934,7 +977,6 @@ int MatrixMultiply(int argc, char **argv,
|
|||
}
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
printf("[globalToShmemAsyncCopy] - Starting...\n");
|
||||
|
||||
|
|
@ -943,11 +985,20 @@ int main(int argc, char **argv) {
|
|||
printf("Usage -device=n (n >= 0 for deviceID)\n");
|
||||
printf(" -wA=WidthA -hA=HeightA (Width x Height of Matrix A)\n");
|
||||
printf(" -wB=WidthB -hB=HeightB (Width x Height of Matrix B)\n");
|
||||
printf(" -kernel=kernel_number (0 - AsyncCopyMultiStageLargeChunk; 1 - AsyncCopyLargeChunk)\n");
|
||||
printf(" (2 - AsyncCopyLargeChunkAWBarrier; 3 - AsyncCopyMultiStageSharedState)\n");
|
||||
printf(" (4 - AsyncCopyMultiStage; 5 - AsyncCopySingleStage; 6 - Naive without memcpy_async)\n");
|
||||
printf(" (7 - NaiveLargeChunk without memcpy_async)\n");
|
||||
printf(" Note: Outer matrix dimensions of A & B matrices must be equal.\n");
|
||||
printf(
|
||||
" -kernel=kernel_number (0 - AsyncCopyMultiStageLargeChunk; 1 - "
|
||||
"AsyncCopyLargeChunk)\n");
|
||||
printf(
|
||||
" (2 - AsyncCopyLargeChunkAWBarrier; 3 - "
|
||||
"AsyncCopyMultiStageSharedState)\n");
|
||||
printf(
|
||||
" (4 - AsyncCopyMultiStage; 5 - "
|
||||
"AsyncCopySingleStage; 6 - Naive without memcpy_async)\n");
|
||||
printf(
|
||||
" (7 - NaiveLargeChunk without "
|
||||
"memcpy_async)\n");
|
||||
printf(
|
||||
" Note: Outer matrix dimensions of A & B matrices must be equal.\n");
|
||||
|
||||
exit(EXIT_SUCCESS);
|
||||
}
|
||||
|
|
@ -990,31 +1041,31 @@ int main(int argc, char **argv) {
|
|||
|
||||
// kernel to run - default (AsyncCopyMultiStageLargeChunk == 0)
|
||||
if (checkCmdLineFlag(argc, (const char **)argv, "kernel")) {
|
||||
int kernel_number = getCmdLineArgumentInt(argc, (const char **)argv, "kernel");
|
||||
if (kernel_number < 8)
|
||||
{
|
||||
int kernel_number =
|
||||
getCmdLineArgumentInt(argc, (const char **)argv, "kernel");
|
||||
if (kernel_number < 8) {
|
||||
selected_kernel = (kernels)kernel_number;
|
||||
}
|
||||
else
|
||||
{
|
||||
printf("Error: kernel number should be between 0 to 6, you have entered %d\n", kernel_number);
|
||||
} else {
|
||||
printf(
|
||||
"Error: kernel number should be between 0 to 6, you have entered "
|
||||
"%d\n",
|
||||
kernel_number);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
}
|
||||
|
||||
int major = 0;
|
||||
checkCudaErrors(cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, dev));
|
||||
if (major < 7)
|
||||
{
|
||||
checkCudaErrors(
|
||||
cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, dev));
|
||||
if (major < 7) {
|
||||
printf("globalToShmemAsyncCopy requires SM 7.0 or higher. Exiting...\n");
|
||||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y,
|
||||
dimsB.x, dimsB.y);
|
||||
printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y, dimsB.x,
|
||||
dimsB.y);
|
||||
|
||||
int matrix_result = MatrixMultiply(argc, argv, dimsA, dimsB, selected_kernel);
|
||||
|
||||
exit(matrix_result);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cudaMallocManaged, cudaDeviceSynchronize, cudaFuncSetAttribute, cudaEventCreate,
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ cudaStreamBeginCapture, cudaStreamEndCapture, cudaGraphCreate, cudaGraphLaunch,
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -109,6 +109,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -105,6 +105,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -100,8 +100,10 @@ int main(int argc, char **argv) {
|
|||
|
||||
double *b = NULL;
|
||||
float *A = NULL;
|
||||
b = (double *)calloc(N_ROWS, sizeof(double));
|
||||
A = (float *)calloc(N_ROWS * N_ROWS, sizeof(float));
|
||||
checkCudaErrors(cudaMallocHost(&b, N_ROWS * sizeof(double)));
|
||||
memset(b, 0, N_ROWS * sizeof(double));
|
||||
checkCudaErrors(cudaMallocHost(&A, N_ROWS * N_ROWS * sizeof(float)));
|
||||
memset(A, 0, N_ROWS * N_ROWS * sizeof(float));
|
||||
|
||||
createLinearSystem(A, b);
|
||||
double *x = NULL;
|
||||
|
|
@ -170,6 +172,9 @@ int main(int argc, char **argv) {
|
|||
checkCudaErrors(cudaFree(d_x));
|
||||
checkCudaErrors(cudaFree(d_x_new));
|
||||
|
||||
checkCudaErrors(cudaFreeHost(A));
|
||||
checkCudaErrors(cudaFreeHost(b));
|
||||
|
||||
printf("&&&& jacobiCudaGraphs %s\n",
|
||||
(fabs(sum - sumGPU) < conv_threshold) ? "PASSED" : "FAILED");
|
||||
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cudaEventCreate, cudaEventRecord, cudaEventQuery, cudaEventDestroy, cudaEventEla
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -104,6 +104,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ cuModuleLoad, cuModuleLoadDataEx, cuModuleGetFunction, cuMemAlloc, cuMemFree, cu
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
|
||||
## Build and Run
|
||||
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -112,6 +112,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -108,6 +108,6 @@
|
|||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -302,14 +302,10 @@ LIBRARIES :=
|
|||
|
||||
################################################################################
|
||||
|
||||
FATBIN_FILE := memMapIpc_kernel${TARGET_SIZE}.fatbin
|
||||
PTX_FILE := memMapIpc_kernel${TARGET_SIZE}.ptx
|
||||
|
||||
# Gencode arguments
|
||||
ifeq ($(TARGET_ARCH),$(filter $(TARGET_ARCH),armv7l aarch64))
|
||||
SMS ?= 35 37 50 52 60 61 70 72 75 80 86
|
||||
else
|
||||
SMS ?= 35 37 50 52 60 61 70 75 80 86
|
||||
endif
|
||||
SMS ?=
|
||||
|
||||
ifeq ($(GENCODE_FLAGS),)
|
||||
# Generate SASS code for each SM architecture listed in $(SMS)
|
||||
|
|
@ -395,7 +391,7 @@ endif
|
|||
# Target rules
|
||||
all: build
|
||||
|
||||
build: memMapIPCDrv $(FATBIN_FILE)
|
||||
build: memMapIPCDrv $(PTX_FILE)
|
||||
|
||||
check.deps:
|
||||
ifeq ($(SAMPLE_ENABLED),0)
|
||||
|
|
@ -404,8 +400,8 @@ else
|
|||
@echo "Sample is ready - all dependencies have been met"
|
||||
endif
|
||||
|
||||
$(FATBIN_FILE): memMapIpc_kernel.cu
|
||||
$(EXEC) $(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -fatbin $<
|
||||
$(PTX_FILE): memMapIpc_kernel.cu
|
||||
$(EXEC) $(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -ptx $<
|
||||
$(EXEC) mkdir -p data
|
||||
$(EXEC) cp -f $@ ./data
|
||||
$(EXEC) mkdir -p ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
|
||||
|
|
@ -426,9 +422,8 @@ run: build
|
|||
$(EXEC) ./memMapIPCDrv
|
||||
|
||||
clean:
|
||||
rm -f memMapIPCDrv helper_multiprocess.o memMapIpc.o data/$(FATBIN_FILE) $(FATBIN_FILE)
|
||||
rm -f memMapIPCDrv helper_multiprocess.o memMapIpc.o data/$(PTX_FILE) $(PTX_FILE)
|
||||
rm -rf ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)/memMapIPCDrv
|
||||
|
||||
rm -rf ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)/$(FATBIN_FILE)
|
||||
rm -rf ../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)/$(PTX_FILE)
|
||||
|
||||
clobber: clean
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ cuModuleLoad, cuModuleLoadDataEx, cuModuleGetFunction, cuLaunchKernel, cuMemcpyD
|
|||
|
||||
## Prerequisites
|
||||
|
||||
Download and install the [CUDA Toolkit 11.2](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Download and install the [CUDA Toolkit 11.3](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
|
||||
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
|
||||
|
||||
## Build and Run
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -67,7 +67,7 @@
|
|||
<OutputFile>$(OutDir)/memMapIPCDrv.exe</OutputFile>
|
||||
</Link>
|
||||
<CudaCompile>
|
||||
<CodeGeneration>compute_35,compute_35;compute_35,sm_35;compute_37,sm_37;compute_50,sm_50;compute_52,sm_52;compute_60,sm_60;compute_61,sm_61;compute_70,sm_70;compute_75,sm_75;compute_80,sm_80;compute_86,sm_86;</CodeGeneration>
|
||||
<CodeGeneration>compute_35,compute_35;</CodeGeneration>
|
||||
<AdditionalOptions>-Xcompiler "/wd 4819" %(AdditionalOptions)</AdditionalOptions>
|
||||
<Include>./;../../Common</Include>
|
||||
<Defines>WIN32</Defines>
|
||||
|
|
@ -105,14 +105,14 @@
|
|||
<ItemGroup>
|
||||
<ClCompile Include="memMapIpc.cpp" />
|
||||
<CudaCompile Include="memMapIpc_kernel.cu">
|
||||
<CompileOut Condition="'$(Platform)'=='x64'">data/%(Filename)64.fatbin</CompileOut>
|
||||
<NvccCompilation>fatbin</NvccCompilation>
|
||||
<CompileOut Condition="'$(Platform)'=='x64'">data/%(Filename)64.ptx</CompileOut>
|
||||
<NvccCompilation>ptx</NvccCompilation>
|
||||
</CudaCompile>
|
||||
<ClCompile Include="../../Common/helper_multiprocess.cpp" />
|
||||
<ClInclude Include="../../Common/helper_multiprocess.h" />
|
||||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@
|
|||
</PropertyGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.props" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.props" />
|
||||
</ImportGroup>
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
|
||||
|
|
@ -63,7 +63,7 @@
|
|||
<OutputFile>$(OutDir)/memMapIPCDrv.exe</OutputFile>
|
||||
</Link>
|
||||
<CudaCompile>
|
||||
<CodeGeneration>compute_35,compute_35;compute_35,sm_35;compute_37,sm_37;compute_50,sm_50;compute_52,sm_52;compute_60,sm_60;compute_61,sm_61;compute_70,sm_70;compute_75,sm_75;compute_80,sm_80;compute_86,sm_86;</CodeGeneration>
|
||||
<CodeGeneration>compute_35,compute_35;</CodeGeneration>
|
||||
<AdditionalOptions>-Xcompiler "/wd 4819" %(AdditionalOptions)</AdditionalOptions>
|
||||
<Include>./;../../Common</Include>
|
||||
<Defines>WIN32</Defines>
|
||||
|
|
@ -101,14 +101,14 @@
|
|||
<ItemGroup>
|
||||
<ClCompile Include="memMapIpc.cpp" />
|
||||
<CudaCompile Include="memMapIpc_kernel.cu">
|
||||
<CompileOut Condition="'$(Platform)'=='x64'">data/%(Filename)64.fatbin</CompileOut>
|
||||
<NvccCompilation>fatbin</NvccCompilation>
|
||||
<CompileOut Condition="'$(Platform)'=='x64'">data/%(Filename)64.ptx</CompileOut>
|
||||
<NvccCompilation>ptx</NvccCompilation>
|
||||
</CudaCompile>
|
||||
<ClCompile Include="../../Common/helper_multiprocess.cpp" />
|
||||
<ClInclude Include="../../Common/helper_multiprocess.h" />
|
||||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.2.targets" />
|
||||
<Import Project="$(CUDAPropsPath)\CUDA 11.3.targets" />
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -64,9 +64,13 @@ typedef struct shmStruct_st {
|
|||
int sense;
|
||||
} shmStruct;
|
||||
|
||||
// define input fatbin file
|
||||
#ifndef FATBIN_FILE
|
||||
#define FATBIN_FILE "memMapIpc_kernel64.fatbin"
|
||||
bool findModulePath(const char *, string &, char **, string &);
|
||||
|
||||
// define input ptx file for different platforms
|
||||
#if defined(_WIN64) || defined(__LP64__)
|
||||
#define PTX_FILE "memMapIpc_kernel64.ptx"
|
||||
#else
|
||||
#define PTX_FILE "memMapIpc_kernel32.ptx"
|
||||
#endif
|
||||
|
||||
// `ipcHandleTypeFlag` specifies the platform specific handle type this sample
|
||||
|
|
@ -255,23 +259,44 @@ static void memMapUnmapAndFreeMemory(CUdeviceptr dptr, size_t size) {
|
|||
|
||||
static void memMapGetDeviceFunction(char **argv) {
|
||||
// first search for the module path before we load the results
|
||||
string module_path;
|
||||
std::ostringstream fatbin;
|
||||
|
||||
if (!findFatbinPath(FATBIN_FILE, module_path, argv, fatbin)) {
|
||||
string module_path, ptx_source;
|
||||
if (!findModulePath(PTX_FILE, module_path, argv, ptx_source)) {
|
||||
if (!findModulePath("memMapIpc_kernel.cubin", module_path, argv,
|
||||
ptx_source)) {
|
||||
printf(
|
||||
"> findModulePath could not find <simpleMemMapIpc> ptx or cubin\n");
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
} else {
|
||||
printf("> initCUDA loading module: <%s>\n", module_path.c_str());
|
||||
}
|
||||
|
||||
if (!fatbin.str().size()) {
|
||||
printf("fatbin file empty. exiting..\n");
|
||||
exit(EXIT_FAILURE);
|
||||
// Create module from binary file (PTX or CUBIN)
|
||||
if (module_path.rfind("ptx") != string::npos) {
|
||||
// in this branch we use compilation with parameters
|
||||
const unsigned int jitNumOptions = 3;
|
||||
CUjit_option *jitOptions = new CUjit_option[jitNumOptions];
|
||||
void **jitOptVals = new void *[jitNumOptions];
|
||||
// set up size of compilation log buffer
|
||||
jitOptions[0] = CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES;
|
||||
int jitLogBufferSize = 1024;
|
||||
jitOptVals[0] = (void *)(size_t)jitLogBufferSize;
|
||||
// set up pointer to the compilation log buffer
|
||||
jitOptions[1] = CU_JIT_INFO_LOG_BUFFER;
|
||||
char *jitLogBuffer = new char[jitLogBufferSize];
|
||||
jitOptVals[1] = jitLogBuffer;
|
||||
// set up pointer to set the Maximum # of registers for a particular kernel
|
||||
jitOptions[2] = CU_JIT_MAX_REGISTERS;
|
||||
int jitRegCount = 32;
|
||||
jitOptVals[2] = (void *)(size_t)jitRegCount;
|
||||
checkCudaErrors(cuModuleLoadDataEx(&cuModule, ptx_source.c_str(),
|
||||
jitNumOptions, jitOptions,
|
||||
(void **)jitOptVals));
|
||||
printf("> PTX JIT log:\n%s\n", jitLogBuffer);
|
||||
} else {
|
||||
checkCudaErrors(cuModuleLoad(&cuModule, module_path.c_str()));
|
||||
}
|
||||
|
||||
// Create module from binary file (FATBIN)
|
||||
checkCudaErrors(cuModuleLoadData(&cuModule, fatbin.str().c_str()));
|
||||
|
||||
// Get function handle from module
|
||||
checkCudaErrors(
|
||||
cuModuleGetFunction(&_memMapIpc_kernel, cuModule, "memMapIpc_kernel"));
|
||||
|
|
@ -585,3 +610,37 @@ int main(int argc, char **argv) {
|
|||
return EXIT_SUCCESS;
|
||||
#endif
|
||||
}
|
||||
|
||||
bool inline findModulePath(const char *module_file, string &module_path,
|
||||
char **argv, string &ptx_source) {
|
||||
char *actual_path = sdkFindFilePath(module_file, argv[0]);
|
||||
|
||||
if (actual_path) {
|
||||
module_path = actual_path;
|
||||
} else {
|
||||
printf("> findModulePath file not found: <%s> \n", module_file);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (module_path.empty()) {
|
||||
printf("> findModulePath could not find file: <%s> \n", module_file);
|
||||
return false;
|
||||
} else {
|
||||
printf("> findModulePath found file at <%s>\n", module_path.c_str());
|
||||
|
||||
if (module_path.rfind(".ptx") != string::npos) {
|
||||
FILE *fp = fopen(module_path.c_str(), "rb");
|
||||
fseek(fp, 0, SEEK_END);
|
||||
int file_size = ftell(fp);
|
||||
char *buf = new char[file_size + 1];
|
||||
fseek(fp, 0, SEEK_SET);
|
||||
fread(buf, sizeof(char), file_size, fp);
|
||||
fclose(fp);
|
||||
buf[file_size] = '\0';
|
||||
ptx_source = buf;
|
||||
delete[] buf;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user