Merge pull request #1122 from emankov/master
[HIPIFY][tests] Add reverse engineered HIP sample "peer2peer"
[ROCm/clr commit: be09662009]
Этот коммит содержится в:
Evgeny Mankov
GitHub
+1
-1
@@ -37,7 +37,7 @@ using namespace std;
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__global__ void matrixTranspose_static_shared(float* out, float* in,
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const int width) {
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// CHECK-NOT: HIP_DYNAMIC_SHARED(int, sharedMem);
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// CHECK-NOT: HIP_DYNAMIC_SHARED(float, sharedMem);
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// CHECK: __shared__ float sharedMem[WIDTH * WIDTH];
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__shared__ float sharedMem[WIDTH * WIDTH];
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+225
@@ -0,0 +1,225 @@
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// RUN: %run_test hipify "%s" "%t" %hipify_args %clang_args
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/*
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Copyright (c) 2015-present Advanced Micro Devices, Inc. All rights reserved.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.
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*/
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#include <iostream>
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// CHECK: #include <hip/hip_runtime.h>
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#include <cuda.h>
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#include <assert.h>
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#define WIDTH 32
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#define NUM (WIDTH * WIDTH)
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#define THREADS_PER_BLOCK_X 4
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#define THREADS_PER_BLOCK_Y 4
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#define THREADS_PER_BLOCK_Z 1
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using namespace std;
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#define KNRM "\x1B[0m"
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#define KRED "\x1B[31m"
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#define failed(...) \
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printf("%serror: ", KRED); \
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printf(__VA_ARGS__); \
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printf("\n"); \
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printf("error: TEST FAILED\n%s", KNRM); \
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abort();
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// CHECK: hipError_t localError = error;
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// CHECK: if ((localError != hipSuccess) && (localError != hipErrorPeerAccessAlreadyEnabled) &&
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// CHECK: (localError != hipErrorPeerAccessNotEnabled )) {
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// CHECK: printf("%serror: '%s'(%d) from %s at %s:%d%s\n", KRED, hipGetErrorString(localError),
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#define CUDACHECK(error) \
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{ \
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cudaError_t localError = error; \
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if ((localError != cudaSuccess) && (localError != cudaErrorPeerAccessAlreadyEnabled) && \
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(localError != cudaErrorPeerAccessNotEnabled )) { \
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printf("%serror: '%s'(%d) from %s at %s:%d%s\n", KRED, cudaGetErrorString(localError), \
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localError, #error, __FILE__, __LINE__, KNRM); \
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failed("API returned error code."); \
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} \
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}
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void checkPeer2PeerSupport() {
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int gpuCount;
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int canAccessPeer;
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// CHECK: CUDACHECK(hipGetDeviceCount(&gpuCount));
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CUDACHECK(cudaGetDeviceCount(&gpuCount));
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for (int currentGpu = 0; currentGpu < gpuCount; currentGpu++) {
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// CHECK: CUDACHECK(hipSetDevice(currentGpu));
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CUDACHECK(cudaSetDevice(currentGpu));
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for (int peerGpu = 0; peerGpu < currentGpu; peerGpu++) {
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if (currentGpu != peerGpu) {
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// CHECK: CUDACHECK(hipDeviceCanAccessPeer(&canAccessPeer, currentGpu, peerGpu));
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CUDACHECK(cudaDeviceCanAccessPeer(&canAccessPeer, currentGpu, peerGpu));
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printf("currentGpu#%d canAccessPeer: peerGpu#%d=%d\n", currentGpu, peerGpu,
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canAccessPeer);
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}
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// CHECK: CUDACHECK(hipSetDevice(peerGpu));
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CUDACHECK(cudaSetDevice(peerGpu));
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// CHECK: CUDACHECK(hipDeviceReset());
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CUDACHECK(cudaDeviceReset());
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}
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// CHECK: CUDACHECK(hipSetDevice(currentGpu));
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CUDACHECK(cudaSetDevice(currentGpu));
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// CHECK: CUDACHECK(hipDeviceReset());
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CUDACHECK(cudaDeviceReset());
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}
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}
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void enablePeer2Peer(int currentGpu, int peerGpu) {
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int canAccessPeer;
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// Must be on a multi-gpu system:
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assert(currentGpu != peerGpu);
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// CHECK: CUDACHECK(hipSetDevice(currentGpu));
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CUDACHECK(cudaSetDevice(currentGpu));
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// CHECK: hipDeviceCanAccessPeer(&canAccessPeer, currentGpu, peerGpu);
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cudaDeviceCanAccessPeer(&canAccessPeer, currentGpu, peerGpu);
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if (canAccessPeer == 1) {
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// CHECK: CUDACHECK(hipDeviceEnablePeerAccess(peerGpu, 0));
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CUDACHECK(cudaDeviceEnablePeerAccess(peerGpu, 0));
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} else
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printf("peer2peer transfer not possible between the selected gpu devices");
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}
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void disablePeer2Peer(int currentGpu, int peerGpu) {
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int canAccessPeer;
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// Must be on a multi-gpu system:
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assert(currentGpu != peerGpu);
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// CHECK: CUDACHECK(hipSetDevice(currentGpu));
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CUDACHECK(cudaSetDevice(currentGpu));
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// CHECK: hipDeviceCanAccessPeer(&canAccessPeer, currentGpu, peerGpu);
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cudaDeviceCanAccessPeer(&canAccessPeer, currentGpu, peerGpu);
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if (canAccessPeer == 1) {
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// CHECK: CUDACHECK(hipDeviceDisablePeerAccess(peerGpu));
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CUDACHECK(cudaDeviceDisablePeerAccess(peerGpu));
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} else
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printf("peer2peer disable not required");
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}
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__global__ void matrixTranspose_static_shared(float* out, float* in,
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const int width) {
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// CHECK-NOT: HIP_DYNAMIC_SHARED(float, sharedMem);
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// CHECK: __shared__ float sharedMem[WIDTH * WIDTH];
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__shared__ float sharedMem[WIDTH * WIDTH];
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int x = blockDim.x * blockIdx.x + threadIdx.x;
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int y = blockDim.y * blockIdx.y + threadIdx.y;
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sharedMem[y * width + x] = in[x * width + y];
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__syncthreads();
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out[y * width + x] = sharedMem[y * width + x];
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}
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__global__ void matrixTranspose_dynamic_shared(float* out, float* in,
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const int width) {
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// declare dynamic shared memory
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// CHECK-NOT: extern __shared__
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// CHECK: HIP_DYNAMIC_SHARED(float, sharedMem)
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extern __shared__ float sharedMem[];
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int x = blockDim.x * blockIdx.x + threadIdx.x;
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int y = blockDim.y * blockIdx.y + threadIdx.y;
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sharedMem[y * width + x] = in[x * width + y];
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__syncthreads();
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out[y * width + x] = sharedMem[y * width + x];
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}
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int main() {
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checkPeer2PeerSupport();
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int gpuCount;
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int currentGpu, peerGpu;
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// CHECK: CUDACHECK(hipGetDeviceCount(&gpuCount));
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CUDACHECK(cudaGetDeviceCount(&gpuCount));
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if (gpuCount < 2) {
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printf("Peer2Peer application requires atleast 2 gpu devices");
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return 0;
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}
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currentGpu = 0;
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peerGpu = (currentGpu + 1);
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printf("currentGpu=%d peerGpu=%d (Total no. of gpu = %d)\n", currentGpu, peerGpu, gpuCount);
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float *data[2], *TransposeMatrix[2], *gpuTransposeMatrix[2], *randArray;
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int width = WIDTH;
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randArray = (float*)malloc(NUM * sizeof(float));
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for (int i = 0; i < NUM; i++) {
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randArray[i] = (float)i * 1.0f;
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}
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enablePeer2Peer(currentGpu, peerGpu);
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// CHECK: CUDACHECK(hipSetDevice(currentGpu));
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CUDACHECK(cudaSetDevice(currentGpu));
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TransposeMatrix[0] = (float*)malloc(NUM * sizeof(float));
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// CHECK: hipMalloc((void**)&gpuTransposeMatrix[0], NUM * sizeof(float));
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cudaMalloc((void**)&gpuTransposeMatrix[0], NUM * sizeof(float));
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// CHECK: hipMalloc((void**)&data[0], NUM * sizeof(float));
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cudaMalloc((void**)&data[0], NUM * sizeof(float));
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// CHECK: hipMemcpy(data[0], randArray, NUM * sizeof(float), hipMemcpyHostToDevice);
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cudaMemcpy(data[0], randArray, NUM * sizeof(float), cudaMemcpyHostToDevice);
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dim3 dimGrid(WIDTH / THREADS_PER_BLOCK_X, WIDTH / THREADS_PER_BLOCK_Y);
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dim3 dimBlock(THREADS_PER_BLOCK_X, THREADS_PER_BLOCK_Y);
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// CHECK: hipLaunchKernelGGL(matrixTranspose_static_shared, dim3(dimGrid), dim3(dimBlock), 0, 0, gpuTransposeMatrix[0], data[0], width);
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matrixTranspose_static_shared <<<dimGrid, dimBlock>>>(gpuTransposeMatrix[0], data[0], width);
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// CHECK: CUDACHECK(hipSetDevice(peerGpu));
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CUDACHECK(cudaSetDevice(peerGpu));
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TransposeMatrix[1] = (float*)malloc(NUM * sizeof(float));
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// CHECK: hipMalloc((void**)&gpuTransposeMatrix[1], NUM * sizeof(float));
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cudaMalloc((void**)&gpuTransposeMatrix[1], NUM * sizeof(float));
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// CHECK: hipMalloc((void**)&data[1], NUM * sizeof(float));
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cudaMalloc((void**)&data[1], NUM * sizeof(float));
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// CHECK: hipMemcpy(data[1], gpuTransposeMatrix[0], NUM * sizeof(float), hipMemcpyDeviceToDevice);
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cudaMemcpy(data[1], gpuTransposeMatrix[0], NUM * sizeof(float), cudaMemcpyDeviceToDevice);
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// CHECK: hipLaunchKernelGGL(matrixTranspose_dynamic_shared, dim3(dimGrid), dim3(dimBlock), sizeof(float) * WIDTH * WIDTH, 0, gpuTransposeMatrix[1], data[1], width);
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matrixTranspose_dynamic_shared <<<dimGrid, dimBlock, sizeof(float) * WIDTH * WIDTH>>>(gpuTransposeMatrix[1], data[1], width);
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// CHECK: hipMemcpy(TransposeMatrix[1], gpuTransposeMatrix[1], NUM * sizeof(float), hipMemcpyDeviceToHost);
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cudaMemcpy(TransposeMatrix[1], gpuTransposeMatrix[1], NUM * sizeof(float), cudaMemcpyDeviceToHost);
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// CHECK: hipDeviceSynchronize();
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cudaDeviceSynchronize();
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disablePeer2Peer(currentGpu, peerGpu);
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// verify the results
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int errors = 0;
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double eps = 1.0E-6;
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for (int i = 0; i < NUM; i++) {
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if (std::abs(randArray[i] - TransposeMatrix[1][i]) > eps) {
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printf("%d cpu: %f gpu peered data %f\n", i, randArray[i], TransposeMatrix[1][i]);
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errors++;
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}
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}
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if (errors != 0) {
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printf("FAILED: %d errors\n", errors);
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} else {
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printf("Peer2Peer PASSED!\n");
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}
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free(randArray);
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for (int i = 0; i < 2; i++) {
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// CHECK: hipFree(data[i]);
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cudaFree(data[i]);
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// CHECK: hipFree(gpuTransposeMatrix[i]);
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cudaFree(gpuTransposeMatrix[i]);
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free(TransposeMatrix[i]);
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}
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// CHECK: CUDACHECK(hipSetDevice(peerGpu));
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CUDACHECK(cudaSetDevice(peerGpu));
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// CHECK: CUDACHECK(hipDeviceReset());
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CUDACHECK(cudaDeviceReset());
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// CHECK: CUDACHECK(hipSetDevice(currentGpu));
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CUDACHECK(cudaSetDevice(currentGpu));
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// CHECK: CUDACHECK(hipDeviceReset());
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CUDACHECK(cudaDeviceReset());
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return 0;
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}
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