196 lines
6.5 KiB
Plaintext
196 lines
6.5 KiB
Plaintext
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#include "mat_mul.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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#define BLOCK_SIZE 16
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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// Block index
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int bx = blockIdx.x;
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int by = blockIdx.y;
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// Thread index
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int tx = threadIdx.x;
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int ty = threadIdx.y;
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// Index of the first sub-matrix of A processed by the block
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int aBegin = K * BLOCK_SIZE * by;
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// Index of the last sub-matrix of A processed by the block
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int aEnd = aBegin + K - 1;
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// Step size used to iterate through the sub-matrices of A
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int aStep = BLOCK_SIZE;
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// Index of the first sub-matrix of B processed by the block
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int bBegin = BLOCK_SIZE * bx;
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// Step size used to iterate through the sub-matrices of B
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int bStep = BLOCK_SIZE * N;
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// Csub is used to store the element of the block sub-matrix
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// that is computed by the thread
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float Csub = 0;
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// Loop over all the sub-matrices of A and B
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// required to compute the block sub-matrix
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for (int a = aBegin, b = bBegin;
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a <= aEnd;
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a += aStep, b += bStep)
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{
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// Declaration of the shared memory array As used to
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// store the sub-matrix of A
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__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
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// Declaration of the shared memory array Bs used to
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// store the sub-matrix of B
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__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
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// Load the matrices from device memory
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// to shared memory; each thread loads
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// one element of each matrix
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As[ty][tx] = A[a + K * ty + tx];
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Bs[ty][tx] = B[b + N * ty + tx];
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// Synchronize to make sure the matrices are loaded
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__syncthreads();
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// Multiply the two matrices together;
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// each thread computes one element
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// of the block sub-matrix
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#pragma unroll
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for (int k = 0; k < BLOCK_SIZE; ++k)
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{
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Csub += As[ty][k] * Bs[k][tx];
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}
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// Synchronize to make sure that the preceding
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// computation is done before loading two new
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// sub-matrices of A and B in the next iteration
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__syncthreads();
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}
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// Write the block sub-matrix to device memory;
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// each thread writes one element
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int c = N * BLOCK_SIZE * by + BLOCK_SIZE * bx;
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C[c + N * ty + tx] = Csub;
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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 M, N, K;
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) {
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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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int rounded_N = (N + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
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int rounded_M = ((Mend[i] - Mbegin[i]) + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
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dim3 blockDim(BLOCK_SIZE, BLOCK_SIZE);
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dim3 gridDim(rounded_N/BLOCK_SIZE, rounded_M/BLOCK_SIZE);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Mend[i] - Mbegin[i], rounded_N, K);
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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( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_init(float *A, float *B, float *C, int _M, int _N, int _K) {
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M = _M, N = _N, K = _K;
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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] = (M / num_devices) * i;
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Mend[i] = (M / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = M;
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// Allocate device memory for each GPU
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int rounded_N = (N + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
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for (int i = 0; i < num_devices; i++) {
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int rounded_M = ((Mend[i] - Mbegin[i]) + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&a_d[i], rounded_M * K * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b_d[i], K * rounded_N * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c_d[i], rounded_M * rounded_N * sizeof(float)) );
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CUDA_CALL( cudaMemset(a_d[i], 0, rounded_M * K * sizeof(float)) );
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CUDA_CALL( cudaMemset(b_d[i], 0, K * rounded_N * sizeof(float)) );
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CUDA_CALL( cudaMemset(c_d[i], 0, rounded_M * rounded_N * sizeof(float)) );
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}
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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( cudaSetDevice(i) );
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CUDA_CALL( cudaMemcpy(a_d[i], A + Mbegin[i] * K,
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(Mend[i] - Mbegin[i]) * K * sizeof(float),
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cudaMemcpyHostToDevice) );
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for (int j = 0; j < K; j++) {
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CUDA_CALL( cudaMemcpy(b_d[i] + j * rounded_N, B + j * N, N * sizeof(float), cudaMemcpyHostToDevice) );
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}
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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( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) {
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// Do any post-matmul cleanup work here.
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int rounded_N = (N + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE;
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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( cudaSetDevice(i) );
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for (int j = 0; j < (Mend[i] - Mbegin[i]); j++) {
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CUDA_CALL( cudaMemcpy(C + Mbegin[i] * N + j * N, c_d[i] + j * rounded_N, N * sizeof(float), cudaMemcpyDeviceToHost) );
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}
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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( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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