mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-28 20:09:15 +08:00
261 lines
8.9 KiB
Plaintext
261 lines
8.9 KiB
Plaintext
/* Copyright (c) 2022, 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.
|
|
*/
|
|
|
|
/*
|
|
* This sample demonstrates a combination of Peer-to-Peer (P2P) and
|
|
* Unified Virtual Address Space (UVA) features new to SDK 4.0
|
|
*/
|
|
|
|
// includes, system
|
|
#include <stdlib.h>
|
|
#include <stdio.h>
|
|
|
|
// CUDA includes
|
|
#include <cuda_runtime.h>
|
|
|
|
// includes, project
|
|
#include <helper_cuda.h>
|
|
#include <helper_functions.h> // helper for shared that are common to CUDA Samples
|
|
|
|
__global__ void SimpleKernel(float *src, float *dst) {
|
|
// Just a dummy kernel, doing enough for us to verify that everything
|
|
// worked
|
|
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
|
dst[idx] = src[idx] * 2.0f;
|
|
}
|
|
|
|
inline bool IsAppBuiltAs64() { return sizeof(void *) == 8; }
|
|
|
|
int main(int argc, char **argv) {
|
|
printf("[%s] - Starting...\n", argv[0]);
|
|
|
|
if (!IsAppBuiltAs64()) {
|
|
printf(
|
|
"%s is only supported with on 64-bit OSs and the application must be "
|
|
"built as a 64-bit target. Test is being waived.\n",
|
|
argv[0]);
|
|
exit(EXIT_WAIVED);
|
|
}
|
|
|
|
// Number of GPUs
|
|
printf("Checking for multiple GPUs...\n");
|
|
int gpu_n;
|
|
checkCudaErrors(cudaGetDeviceCount(&gpu_n));
|
|
printf("CUDA-capable device count: %i\n", gpu_n);
|
|
|
|
if (gpu_n < 2) {
|
|
printf(
|
|
"Two or more GPUs with Peer-to-Peer access capability are required for "
|
|
"%s.\n",
|
|
argv[0]);
|
|
printf("Waiving test.\n");
|
|
exit(EXIT_WAIVED);
|
|
}
|
|
|
|
// Query device properties
|
|
cudaDeviceProp prop[64];
|
|
int gpuid[2]; // we want to find the first two GPU's that can support P2P
|
|
|
|
for (int i = 0; i < gpu_n; i++) {
|
|
checkCudaErrors(cudaGetDeviceProperties(&prop[i], i));
|
|
}
|
|
// Check possibility for peer access
|
|
printf("\nChecking GPU(s) for support of peer to peer memory access...\n");
|
|
|
|
int can_access_peer;
|
|
int p2pCapableGPUs[2]; // We take only 1 pair of P2P capable GPUs
|
|
p2pCapableGPUs[0] = p2pCapableGPUs[1] = -1;
|
|
|
|
// Show all the combinations of supported P2P GPUs
|
|
for (int i = 0; i < gpu_n; i++) {
|
|
for (int j = 0; j < gpu_n; j++) {
|
|
if (i == j) {
|
|
continue;
|
|
}
|
|
checkCudaErrors(cudaDeviceCanAccessPeer(&can_access_peer, i, j));
|
|
printf("> Peer access from %s (GPU%d) -> %s (GPU%d) : %s\n", prop[i].name,
|
|
i, prop[j].name, j, can_access_peer ? "Yes" : "No");
|
|
if (can_access_peer && p2pCapableGPUs[0] == -1) {
|
|
p2pCapableGPUs[0] = i;
|
|
p2pCapableGPUs[1] = j;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (p2pCapableGPUs[0] == -1 || p2pCapableGPUs[1] == -1) {
|
|
printf(
|
|
"Two or more GPUs with Peer-to-Peer access capability are required for "
|
|
"%s.\n",
|
|
argv[0]);
|
|
printf(
|
|
"Peer to Peer access is not available amongst GPUs in the system, "
|
|
"waiving test.\n");
|
|
|
|
exit(EXIT_WAIVED);
|
|
}
|
|
|
|
// Use first pair of p2p capable GPUs detected.
|
|
gpuid[0] = p2pCapableGPUs[0];
|
|
gpuid[1] = p2pCapableGPUs[1];
|
|
|
|
// Enable peer access
|
|
printf("Enabling peer access between GPU%d and GPU%d...\n", gpuid[0],
|
|
gpuid[1]);
|
|
checkCudaErrors(cudaSetDevice(gpuid[0]));
|
|
checkCudaErrors(cudaDeviceEnablePeerAccess(gpuid[1], 0));
|
|
checkCudaErrors(cudaSetDevice(gpuid[1]));
|
|
checkCudaErrors(cudaDeviceEnablePeerAccess(gpuid[0], 0));
|
|
|
|
// Allocate buffers
|
|
const size_t buf_size = 1024 * 1024 * 16 * sizeof(float);
|
|
printf("Allocating buffers (%iMB on GPU%d, GPU%d and CPU Host)...\n",
|
|
int(buf_size / 1024 / 1024), gpuid[0], gpuid[1]);
|
|
checkCudaErrors(cudaSetDevice(gpuid[0]));
|
|
float *g0;
|
|
checkCudaErrors(cudaMalloc(&g0, buf_size));
|
|
checkCudaErrors(cudaSetDevice(gpuid[1]));
|
|
float *g1;
|
|
checkCudaErrors(cudaMalloc(&g1, buf_size));
|
|
float *h0;
|
|
checkCudaErrors(
|
|
cudaMallocHost(&h0, buf_size)); // Automatically portable with UVA
|
|
|
|
// Create CUDA event handles
|
|
printf("Creating event handles...\n");
|
|
cudaEvent_t start_event, stop_event;
|
|
float time_memcpy;
|
|
int eventflags = cudaEventBlockingSync;
|
|
checkCudaErrors(cudaEventCreateWithFlags(&start_event, eventflags));
|
|
checkCudaErrors(cudaEventCreateWithFlags(&stop_event, eventflags));
|
|
|
|
// P2P memcopy() benchmark
|
|
checkCudaErrors(cudaEventRecord(start_event, 0));
|
|
|
|
for (int i = 0; i < 100; i++) {
|
|
// With UVA we don't need to specify source and target devices, the
|
|
// runtime figures this out by itself from the pointers
|
|
// Ping-pong copy between GPUs
|
|
if (i % 2 == 0) {
|
|
checkCudaErrors(cudaMemcpy(g1, g0, buf_size, cudaMemcpyDefault));
|
|
} else {
|
|
checkCudaErrors(cudaMemcpy(g0, g1, buf_size, cudaMemcpyDefault));
|
|
}
|
|
}
|
|
|
|
checkCudaErrors(cudaEventRecord(stop_event, 0));
|
|
checkCudaErrors(cudaEventSynchronize(stop_event));
|
|
checkCudaErrors(cudaEventElapsedTime(&time_memcpy, start_event, stop_event));
|
|
printf("cudaMemcpyPeer / cudaMemcpy between GPU%d and GPU%d: %.2fGB/s\n",
|
|
gpuid[0], gpuid[1],
|
|
(1.0f / (time_memcpy / 1000.0f)) * ((100.0f * buf_size)) / 1024.0f /
|
|
1024.0f / 1024.0f);
|
|
|
|
// Prepare host buffer and copy to GPU 0
|
|
printf("Preparing host buffer and memcpy to GPU%d...\n", gpuid[0]);
|
|
|
|
for (int i = 0; i < buf_size / sizeof(float); i++) {
|
|
h0[i] = float(i % 4096);
|
|
}
|
|
|
|
checkCudaErrors(cudaSetDevice(gpuid[0]));
|
|
checkCudaErrors(cudaMemcpy(g0, h0, buf_size, cudaMemcpyDefault));
|
|
|
|
// Kernel launch configuration
|
|
const dim3 threads(512, 1);
|
|
const dim3 blocks((buf_size / sizeof(float)) / threads.x, 1);
|
|
|
|
// Run kernel on GPU 1, reading input from the GPU 0 buffer, writing
|
|
// output to the GPU 1 buffer
|
|
printf(
|
|
"Run kernel on GPU%d, taking source data from GPU%d and writing to "
|
|
"GPU%d...\n",
|
|
gpuid[1], gpuid[0], gpuid[1]);
|
|
checkCudaErrors(cudaSetDevice(gpuid[1]));
|
|
SimpleKernel<<<blocks, threads>>>(g0, g1);
|
|
|
|
checkCudaErrors(cudaDeviceSynchronize());
|
|
|
|
// Run kernel on GPU 0, reading input from the GPU 1 buffer, writing
|
|
// output to the GPU 0 buffer
|
|
printf(
|
|
"Run kernel on GPU%d, taking source data from GPU%d and writing to "
|
|
"GPU%d...\n",
|
|
gpuid[0], gpuid[1], gpuid[0]);
|
|
checkCudaErrors(cudaSetDevice(gpuid[0]));
|
|
SimpleKernel<<<blocks, threads>>>(g1, g0);
|
|
|
|
checkCudaErrors(cudaDeviceSynchronize());
|
|
|
|
// Copy data back to host and verify
|
|
printf("Copy data back to host from GPU%d and verify results...\n", gpuid[0]);
|
|
checkCudaErrors(cudaMemcpy(h0, g0, buf_size, cudaMemcpyDefault));
|
|
|
|
int error_count = 0;
|
|
|
|
for (int i = 0; i < buf_size / sizeof(float); i++) {
|
|
// Re-generate input data and apply 2x '* 2.0f' computation of both
|
|
// kernel runs
|
|
if (h0[i] != float(i % 4096) * 2.0f * 2.0f) {
|
|
printf("Verification error @ element %i: val = %f, ref = %f\n", i, h0[i],
|
|
(float(i % 4096) * 2.0f * 2.0f));
|
|
|
|
if (error_count++ > 10) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Disable peer access (also unregisters memory for non-UVA cases)
|
|
printf("Disabling peer access...\n");
|
|
checkCudaErrors(cudaSetDevice(gpuid[0]));
|
|
checkCudaErrors(cudaDeviceDisablePeerAccess(gpuid[1]));
|
|
checkCudaErrors(cudaSetDevice(gpuid[1]));
|
|
checkCudaErrors(cudaDeviceDisablePeerAccess(gpuid[0]));
|
|
|
|
// Cleanup and shutdown
|
|
printf("Shutting down...\n");
|
|
checkCudaErrors(cudaEventDestroy(start_event));
|
|
checkCudaErrors(cudaEventDestroy(stop_event));
|
|
checkCudaErrors(cudaSetDevice(gpuid[0]));
|
|
checkCudaErrors(cudaFree(g0));
|
|
checkCudaErrors(cudaSetDevice(gpuid[1]));
|
|
checkCudaErrors(cudaFree(g1));
|
|
checkCudaErrors(cudaFreeHost(h0));
|
|
|
|
for (int i = 0; i < gpu_n; i++) {
|
|
checkCudaErrors(cudaSetDevice(i));
|
|
}
|
|
|
|
if (error_count != 0) {
|
|
printf("Test failed!\n");
|
|
exit(EXIT_FAILURE);
|
|
} else {
|
|
printf("Test passed\n");
|
|
exit(EXIT_SUCCESS);
|
|
}
|
|
}
|