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Remove outdated bandwidthTest sample
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### CUDA 12.9
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* Updated toolchain for cross-compilation for Tegra Linux platforms.
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* Repository has been updated with consistent code formatting across all samples
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* Many small code tweaks and bug fixes (see commit history for details)
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* Removed the following outdated samples:
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* `1_Utilities`
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* `bandwidthTest` - this sample was out of date and did not produce accurate results. For bandwidth
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testing of NVIDIA GPU platforms, please refer to [NVBandwidth](https://github.com/NVIDIA/nvbandwidth)
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### CUDA 12.8
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* Updated build system across the repository to CMake. Removed Visual Studio project files and Makefiles.
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{
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"configurations": [
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{
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"name": "Linux",
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"includePath": [
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"${workspaceFolder}/**",
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"${workspaceFolder}/../../../Common"
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],
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"defines": [],
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"compilerPath": "/usr/local/cuda/bin/nvcc",
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"cStandard": "gnu17",
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"cppStandard": "gnu++14",
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"intelliSenseMode": "linux-gcc-x64",
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"configurationProvider": "ms-vscode.makefile-tools"
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}
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],
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"version": 4
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}
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{
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"recommendations": [
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"nvidia.nsight-vscode-edition",
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"ms-vscode.cpptools",
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"ms-vscode.makefile-tools"
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]
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}
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{
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"configurations": [
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{
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"name": "CUDA C++: Launch",
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"type": "cuda-gdb",
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"request": "launch",
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"program": "${workspaceFolder}/bandwidthTest"
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}
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]
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}
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{
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"version": "2.0.0",
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"tasks": [
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{
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"label": "sample",
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"type": "shell",
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"command": "make dbg=1",
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"problemMatcher": ["$nvcc"],
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"group": {
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"kind": "build",
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"isDefault": true
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}
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}
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]
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}
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cmake_minimum_required(VERSION 3.20)
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list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules")
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project(bandwidthTest LANGUAGES C CXX CUDA)
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find_package(CUDAToolkit REQUIRED)
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set(CMAKE_POSITION_INDEPENDENT_CODE ON)
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set(CMAKE_CUDA_ARCHITECTURES 50 52 60 61 70 72 75 80 86 87 89 90 100 101 120)
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets")
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if(ENABLE_CUDA_DEBUG)
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets)
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else()
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set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option)
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endif()
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# Include directories and libraries
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include_directories(../../../Common)
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# Source file
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# Add target for bandwidthTest
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add_executable(bandwidthTest bandwidthTest.cu)
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target_compile_options(bandwidthTest PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>)
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target_compile_features(bandwidthTest PRIVATE cxx_std_17 cuda_std_17)
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set_target_properties(bandwidthTest PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
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# bandwidthTest - Bandwidth Test
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## Description
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This is a simple test program to measure the memcopy bandwidth of the GPU and memcpy bandwidth across PCI-e. This test application is capable of measuring device to device copy bandwidth, host to device copy bandwidth for pageable and page-locked memory, and device to host copy bandwidth for pageable and page-locked memory.
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## Key Concepts
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CUDA Streams and Events, Performance Strategies
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## Supported SM Architectures
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[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus)
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## Supported OSes
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Linux, Windows
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## Supported CPU Architecture
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x86_64, armv7l
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## CUDA APIs involved
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### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
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cudaHostAlloc, cudaMemcpy, cudaMalloc, cudaMemcpyAsync, cudaFree, cudaGetErrorString, cudaMallocHost, cudaSetDevice, cudaGetDeviceProperties, cudaDeviceSynchronize, cudaEventRecord, cudaFreeHost, cudaEventDestroy, cudaEventElapsedTime, cudaGetDeviceCount, cudaEventCreate
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## Prerequisites
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Download and install the [CUDA Toolkit 12.5](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
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## References (for more details)
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