cuda-samples/Samples/simpleZeroCopy/simpleZeroCopy.cu
2021-10-21 16:34:49 +05:30

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/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
// System includes
#include <assert.h>
#include <stdio.h>
// CUDA runtime
#include <cuda_runtime.h>
// helper functions and utilities to work with CUDA
#include <helper_cuda.h>
#include <helper_functions.h>
#ifndef MAX
#define MAX(a, b) (a > b ? a : b)
#endif
/* Add two vectors on the GPU */
__global__ void vectorAddGPU(float *a, float *b, float *c, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
c[idx] = a[idx] + b[idx];
}
}
// Allocate generic memory with malloc() and pin it laster instead of using
// cudaHostAlloc()
bool bPinGenericMemory = false;
// Macro to aligned up to the memory size in question
#define MEMORY_ALIGNMENT 4096
#define ALIGN_UP(x, size) (((size_t)x + (size - 1)) & (~(size - 1)))
int main(int argc, char **argv) {
int n, nelem, deviceCount;
int idev = 0; // use default device 0
char *device = NULL;
unsigned int flags;
size_t bytes;
float *a, *b, *c; // Pinned memory allocated on the CPU
float *a_UA, *b_UA, *c_UA; // Non-4K Aligned Pinned memory on the CPU
float *d_a, *d_b, *d_c; // Device pointers for mapped memory
float errorNorm, refNorm, ref, diff;
cudaDeviceProp deviceProp;
if (checkCmdLineFlag(argc, (const char **)argv, "help")) {
printf("Usage: simpleZeroCopy [OPTION]\n\n");
printf("Options:\n");
printf(" --device=[device #] Specify the device to be used\n");
printf(
" --use_generic_memory (optional) use generic page-aligned for system "
"memory\n");
return EXIT_SUCCESS;
}
/* Get the device selected by the user or default to 0, and then set it. */
if (getCmdLineArgumentString(argc, (const char **)argv, "device", &device)) {
cudaGetDeviceCount(&deviceCount);
idev = atoi(device);
if (idev >= deviceCount || idev < 0) {
fprintf(stderr,
"Device number %d is invalid, will use default CUDA device 0.\n",
idev);
idev = 0;
}
}
// if GPU found supports SM 1.2, then continue, otherwise we exit
if (!checkCudaCapabilities(1, 2)) {
exit(EXIT_SUCCESS);
}
if (checkCmdLineFlag(argc, (const char **)argv, "use_generic_memory")) {
#if defined(__APPLE__) || defined(MACOSX)
bPinGenericMemory = false; // Generic Pinning of System Paged memory is not
// currently supported on Mac OSX
#else
bPinGenericMemory = true;
#endif
}
if (bPinGenericMemory) {
printf("> Using Generic System Paged Memory (malloc)\n");
} else {
printf("> Using CUDA Host Allocated (cudaHostAlloc)\n");
}
checkCudaErrors(cudaSetDevice(idev));
/* Verify the selected device supports mapped memory and set the device
flags for mapping host memory. */
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, idev));
#if CUDART_VERSION >= 2020
if (!deviceProp.canMapHostMemory) {
fprintf(stderr, "Device %d does not support mapping CPU host memory!\n",
idev);
exit(EXIT_SUCCESS);
}
checkCudaErrors(cudaSetDeviceFlags(cudaDeviceMapHost));
#else
fprintf(stderr,
"CUDART version %d.%d does not support "
"<cudaDeviceProp.canMapHostMemory> field\n",
, CUDART_VERSION / 1000, (CUDART_VERSION % 100) / 10);
exit(EXIT_SUCCESS);
#endif
#if CUDART_VERSION < 4000
if (bPinGenericMemory) {
fprintf(
stderr,
"CUDART version %d.%d does not support <cudaHostRegister> function\n",
CUDART_VERSION / 1000, (CUDART_VERSION % 100) / 10);
exit(EXIT_SUCCESS);
}
#endif
/* Allocate mapped CPU memory. */
nelem = 1048576;
bytes = nelem * sizeof(float);
if (bPinGenericMemory) {
#if CUDART_VERSION >= 4000
a_UA = (float *)malloc(bytes + MEMORY_ALIGNMENT);
b_UA = (float *)malloc(bytes + MEMORY_ALIGNMENT);
c_UA = (float *)malloc(bytes + MEMORY_ALIGNMENT);
// We need to ensure memory is aligned to 4K (so we will need to padd memory
// accordingly)
a = (float *)ALIGN_UP(a_UA, MEMORY_ALIGNMENT);
b = (float *)ALIGN_UP(b_UA, MEMORY_ALIGNMENT);
c = (float *)ALIGN_UP(c_UA, MEMORY_ALIGNMENT);
checkCudaErrors(cudaHostRegister(a, bytes, cudaHostRegisterMapped));
checkCudaErrors(cudaHostRegister(b, bytes, cudaHostRegisterMapped));
checkCudaErrors(cudaHostRegister(c, bytes, cudaHostRegisterMapped));
#endif
} else {
#if CUDART_VERSION >= 2020
flags = cudaHostAllocMapped;
checkCudaErrors(cudaHostAlloc((void **)&a, bytes, flags));
checkCudaErrors(cudaHostAlloc((void **)&b, bytes, flags));
checkCudaErrors(cudaHostAlloc((void **)&c, bytes, flags));
#endif
}
/* Initialize the vectors. */
for (n = 0; n < nelem; n++) {
a[n] = rand() / (float)RAND_MAX;
b[n] = rand() / (float)RAND_MAX;
}
/* Get the device pointers for the pinned CPU memory mapped into the GPU
memory space. */
#if CUDART_VERSION >= 2020
checkCudaErrors(cudaHostGetDevicePointer((void **)&d_a, (void *)a, 0));
checkCudaErrors(cudaHostGetDevicePointer((void **)&d_b, (void *)b, 0));
checkCudaErrors(cudaHostGetDevicePointer((void **)&d_c, (void *)c, 0));
#endif
/* Call the GPU kernel using the CPU pointers residing in CPU mapped memory.
*/
printf("> vectorAddGPU kernel will add vectors using mapped CPU memory...\n");
dim3 block(256);
dim3 grid((unsigned int)ceil(nelem / (float)block.x));
vectorAddGPU<<<grid, block>>>(d_a, d_b, d_c, nelem);
checkCudaErrors(cudaDeviceSynchronize());
getLastCudaError("vectorAddGPU() execution failed");
/* Compare the results */
printf("> Checking the results from vectorAddGPU() ...\n");
errorNorm = 0.f;
refNorm = 0.f;
for (n = 0; n < nelem; n++) {
ref = a[n] + b[n];
diff = c[n] - ref;
errorNorm += diff * diff;
refNorm += ref * ref;
}
errorNorm = (float)sqrt((double)errorNorm);
refNorm = (float)sqrt((double)refNorm);
/* Memory clean up */
printf("> Releasing CPU memory...\n");
if (bPinGenericMemory) {
#if CUDART_VERSION >= 4000
checkCudaErrors(cudaHostUnregister(a));
checkCudaErrors(cudaHostUnregister(b));
checkCudaErrors(cudaHostUnregister(c));
free(a_UA);
free(b_UA);
free(c_UA);
#endif
} else {
#if CUDART_VERSION >= 2020
checkCudaErrors(cudaFreeHost(a));
checkCudaErrors(cudaFreeHost(b));
checkCudaErrors(cudaFreeHost(c));
#endif
}
exit(errorNorm / refNorm < 1.e-6f ? EXIT_SUCCESS : EXIT_FAILURE);
}