mirror of
https://github.com/NVIDIA/cuda-samples.git
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248 lines
7.8 KiB
Plaintext
248 lines
7.8 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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// System includes
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#include <assert.h>
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#include <stdio.h>
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// CUDA runtime
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#include <cuda_runtime.h>
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// helper functions and utilities to work with CUDA
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#include <helper_cuda.h>
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#include <helper_functions.h>
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#ifndef MAX
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#define MAX(a, b) (a > b ? a : b)
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#endif
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/* Add two vectors on the GPU */
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__global__ void vectorAddGPU(float *a, float *b, float *c, int N) {
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < N) {
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c[idx] = a[idx] + b[idx];
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}
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}
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// Allocate generic memory with malloc() and pin it laster instead of using
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// cudaHostAlloc()
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bool bPinGenericMemory = false;
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// Macro to aligned up to the memory size in question
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#define MEMORY_ALIGNMENT 4096
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#define ALIGN_UP(x, size) (((size_t)x + (size - 1)) & (~(size - 1)))
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int main(int argc, char **argv) {
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int n, nelem, deviceCount;
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int idev = 0; // use default device 0
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char *device = NULL;
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unsigned int flags;
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size_t bytes;
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float *a, *b, *c; // Pinned memory allocated on the CPU
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float *a_UA, *b_UA, *c_UA; // Non-4K Aligned Pinned memory on the CPU
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float *d_a, *d_b, *d_c; // Device pointers for mapped memory
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float errorNorm, refNorm, ref, diff;
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cudaDeviceProp deviceProp;
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if (checkCmdLineFlag(argc, (const char **)argv, "help")) {
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printf("Usage: simpleZeroCopy [OPTION]\n\n");
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printf("Options:\n");
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printf(" --device=[device #] Specify the device to be used\n");
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printf(
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" --use_generic_memory (optional) use generic page-aligned for system "
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"memory\n");
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return EXIT_SUCCESS;
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}
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/* Get the device selected by the user or default to 0, and then set it. */
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if (getCmdLineArgumentString(argc, (const char **)argv, "device", &device)) {
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cudaGetDeviceCount(&deviceCount);
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idev = atoi(device);
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if (idev >= deviceCount || idev < 0) {
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fprintf(stderr,
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"Device number %d is invalid, will use default CUDA device 0.\n",
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idev);
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idev = 0;
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}
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}
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// if GPU found supports SM 1.2, then continue, otherwise we exit
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if (!checkCudaCapabilities(1, 2)) {
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exit(EXIT_SUCCESS);
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "use_generic_memory")) {
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#if defined(__APPLE__) || defined(MACOSX)
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bPinGenericMemory = false; // Generic Pinning of System Paged memory is not
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// currently supported on Mac OSX
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#else
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bPinGenericMemory = true;
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#endif
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}
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if (bPinGenericMemory) {
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printf("> Using Generic System Paged Memory (malloc)\n");
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} else {
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printf("> Using CUDA Host Allocated (cudaHostAlloc)\n");
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}
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checkCudaErrors(cudaSetDevice(idev));
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/* Verify the selected device supports mapped memory and set the device
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flags for mapping host memory. */
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, idev));
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#if CUDART_VERSION >= 2020
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if (!deviceProp.canMapHostMemory) {
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fprintf(stderr, "Device %d does not support mapping CPU host memory!\n",
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idev);
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exit(EXIT_SUCCESS);
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}
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checkCudaErrors(cudaSetDeviceFlags(cudaDeviceMapHost));
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#else
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fprintf(stderr,
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"CUDART version %d.%d does not support "
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"<cudaDeviceProp.canMapHostMemory> field\n",
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, CUDART_VERSION / 1000, (CUDART_VERSION % 100) / 10);
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exit(EXIT_SUCCESS);
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#endif
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#if CUDART_VERSION < 4000
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if (bPinGenericMemory) {
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fprintf(
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stderr,
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"CUDART version %d.%d does not support <cudaHostRegister> function\n",
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CUDART_VERSION / 1000, (CUDART_VERSION % 100) / 10);
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exit(EXIT_SUCCESS);
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}
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#endif
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/* Allocate mapped CPU memory. */
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nelem = 1048576;
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bytes = nelem * sizeof(float);
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if (bPinGenericMemory) {
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#if CUDART_VERSION >= 4000
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a_UA = (float *)malloc(bytes + MEMORY_ALIGNMENT);
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b_UA = (float *)malloc(bytes + MEMORY_ALIGNMENT);
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c_UA = (float *)malloc(bytes + MEMORY_ALIGNMENT);
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// We need to ensure memory is aligned to 4K (so we will need to padd memory
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// accordingly)
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a = (float *)ALIGN_UP(a_UA, MEMORY_ALIGNMENT);
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b = (float *)ALIGN_UP(b_UA, MEMORY_ALIGNMENT);
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c = (float *)ALIGN_UP(c_UA, MEMORY_ALIGNMENT);
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checkCudaErrors(cudaHostRegister(a, bytes, cudaHostRegisterMapped));
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checkCudaErrors(cudaHostRegister(b, bytes, cudaHostRegisterMapped));
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checkCudaErrors(cudaHostRegister(c, bytes, cudaHostRegisterMapped));
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#endif
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} else {
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#if CUDART_VERSION >= 2020
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flags = cudaHostAllocMapped;
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checkCudaErrors(cudaHostAlloc((void **)&a, bytes, flags));
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checkCudaErrors(cudaHostAlloc((void **)&b, bytes, flags));
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checkCudaErrors(cudaHostAlloc((void **)&c, bytes, flags));
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#endif
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}
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/* Initialize the vectors. */
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for (n = 0; n < nelem; n++) {
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a[n] = rand() / (float)RAND_MAX;
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b[n] = rand() / (float)RAND_MAX;
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}
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/* Get the device pointers for the pinned CPU memory mapped into the GPU
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memory space. */
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#if CUDART_VERSION >= 2020
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checkCudaErrors(cudaHostGetDevicePointer((void **)&d_a, (void *)a, 0));
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checkCudaErrors(cudaHostGetDevicePointer((void **)&d_b, (void *)b, 0));
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checkCudaErrors(cudaHostGetDevicePointer((void **)&d_c, (void *)c, 0));
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#endif
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/* Call the GPU kernel using the CPU pointers residing in CPU mapped memory.
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*/
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printf("> vectorAddGPU kernel will add vectors using mapped CPU memory...\n");
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dim3 block(256);
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dim3 grid((unsigned int)ceil(nelem / (float)block.x));
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vectorAddGPU<<<grid, block>>>(d_a, d_b, d_c, nelem);
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checkCudaErrors(cudaDeviceSynchronize());
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getLastCudaError("vectorAddGPU() execution failed");
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/* Compare the results */
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printf("> Checking the results from vectorAddGPU() ...\n");
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errorNorm = 0.f;
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refNorm = 0.f;
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for (n = 0; n < nelem; n++) {
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ref = a[n] + b[n];
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diff = c[n] - ref;
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errorNorm += diff * diff;
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refNorm += ref * ref;
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}
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errorNorm = (float)sqrt((double)errorNorm);
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refNorm = (float)sqrt((double)refNorm);
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/* Memory clean up */
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printf("> Releasing CPU memory...\n");
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if (bPinGenericMemory) {
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#if CUDART_VERSION >= 4000
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checkCudaErrors(cudaHostUnregister(a));
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checkCudaErrors(cudaHostUnregister(b));
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checkCudaErrors(cudaHostUnregister(c));
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free(a_UA);
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free(b_UA);
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free(c_UA);
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#endif
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} else {
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#if CUDART_VERSION >= 2020
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checkCudaErrors(cudaFreeHost(a));
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checkCudaErrors(cudaFreeHost(b));
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checkCudaErrors(cudaFreeHost(c));
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#endif
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}
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exit(errorNorm / refNorm < 1.e-6f ? EXIT_SUCCESS : EXIT_FAILURE);
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}
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