149 lines
4.7 KiB
Plaintext
149 lines
4.7 KiB
Plaintext
#include "convolution.h"
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#include <cstdio>
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#include <cuda_runtime.h>
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#define CUDA_CALL(f) \
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{ \
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cudaError_t err = (f); \
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if (err != cudaSuccess) { \
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fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \
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err, cudaGetErrorString(err)); \
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exit(1); \
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} \
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}
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#define MAX_NUM_GPU 4
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int num_devices = 0;
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__global__ void conv_kernel(float *input, float *filter, float *output,
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int N, int C, int H, int W,
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int K, int R, int S,
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int pad, int dilation, int stride) {
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int n = blockIdx.x;
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int k = blockIdx.y;;
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int oh = blockIdx.z;
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int ow = threadIdx.x;
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int OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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int OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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float o = 0.f;
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for (int c = 0; c < C; ++c) {
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for (int r = 0; r < R; ++r) {
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for (int s = 0; s < S; ++s) {
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int h = oh * stride - pad + r * dilation;
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int w = ow * stride - pad + s * dilation;
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if (h < 0 || h >= H || w < 0 || w >= W) continue;
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float i = input[n * C * H * W + c * H * W + h * W + w];
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float f = filter[k * C * R * S + c * R * S + r * S + s];
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o += i * f;
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}
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}
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}
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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}
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// Array of device (GPU) pointers
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static float *a_d[MAX_NUM_GPU];
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static float *b_d[MAX_NUM_GPU];
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static float *c_d[MAX_NUM_GPU];
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static int N, C, H, W;
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static int K, R, S;
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static int OH, OW;
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static int pad;
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static int dilation;
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static int stride;
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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void cuda_conv() {
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// Launch kernel on every GPU
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for (int i = 0; i < num_devices; i++) {
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dim3 blockDim(OW, 1, 1);
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dim3 gridDim(Mend[i] - Mbegin[i], K, OH);
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CUDA_CALL( cudaSetDevice(i) );
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conv_kernel<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Mend[i]-Mbegin[i], C, H, W, K, R, S, pad, dilation, stride);
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void cuda_mem_init(int _N, int _C, int _H, int _W, int _K, int _R, int _S, int _pad, int _dilation, int _stride) {
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N = _N; C = _C; H = _H; W = _W;
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K = _K; R = _R; S = _S;
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pad = _pad;
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dilation = _dilation;
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stride = _stride;
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OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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//printf("Using %d devices\n", num_devices);
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for (int i = 0; i < num_devices; i++) {
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cudaDeviceProp prop;
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CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
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// Try printing more detailed information here
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//printf("[GPU %d] %s\n", i, prop.name);
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}
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if (num_devices <= 0) {
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printf("No CUDA device found. Aborting\n");
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exit(1);
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}
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// Setup problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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Mbegin[i] = (N / num_devices) * i;
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Mend[i] = (N / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = N;
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// Allocate device memory for each GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&a_d[i], (Mend[i] - Mbegin[i]) * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b_d[i], K * C * R * S * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c_d[i], (Mend[i] - Mbegin[i]) * K * OH * OW * sizeof(float)) );
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}
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}
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void cuda_conv_init(float *input, float *filter, float *output) {
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// Upload A and B matrix to every GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(a_d[i], input + Mbegin[i] * C * H * W,
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(Mend[i] - Mbegin[i]) * C * H * W * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(b_d[i], filter, K * C * R * S * sizeof(float), cudaMemcpyHostToDevice) );
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void cuda_conv_final(float *output) {
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// Do any post-matmul cleanup work here.
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// Download C matrix from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(output + Mbegin[i] * K * OH * OW, c_d[i],
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(Mend[i] - Mbegin[i]) * K * OH * OW * sizeof(float),
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cudaMemcpyDeviceToHost) );
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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