222 lines
6.8 KiB
Plaintext
222 lines
6.8 KiB
Plaintext
#include "mat_mul.h"
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#include <cstdio>
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#include <cuda_runtime.h>
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#define BLOCK_DIM 4
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#define TS 32
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#define WPT 16
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#define RTS (TS/WPT)
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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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// n->K p->N m->M
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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const int row = threadIdx.x;//); // row index of C
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const int col = threadIdx.y;//); // column index of C
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const int global_row = (blockDim.x*WPT)* blockIdx.x + threadIdx.x;
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const int global_col = blockDim.y * blockIdx.y + threadIdx.y;
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__shared__ float subA[TS][TS];
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__shared__ float subB[TS][TS];
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float accum[WPT];
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for (int w = 0; w < WPT; w++) {
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accum[w] = 0.0f;
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}
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const int num_tiles = (K+TS-1)/TS;
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for (int t = 0; t < num_tiles; t++) {
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for (int w = 0; w < WPT; w++) {
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const int tiledRow = TS*t + row;
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const int tiledCol = TS*t + col;
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/*if (M <= global_row) subA[row + w*RTS][col] = 0.0f;
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else*/
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if (K <= tiledCol) subA[row + w*RTS][col] = 0.0f;
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else if (M <= (global_row + w*RTS)) subA[row + w*RTS][col] = 0.0f;
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else
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subA[row + w*RTS][col] = A[(global_row + w*RTS)*K + tiledCol];
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if (N <= global_col) subB[row + w*RTS][col] = 0.0f;
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//else if (K <= tiledRow) subB[row + w*RTS][col] = 0.0f;
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else if (K <= (tiledRow + w*RTS)) subB[row + w*RTS][col] = 0.0f;
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else
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subB[row + w*RTS][col] = B[(tiledRow + w*RTS)*N + global_col];
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}
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__syncthreads();
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//barrier(CLK_LOCAL_MEM_FENCE);
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for (int k = 0; k < TS; k++) {
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for (int w = 0; w < WPT; w++) {
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accum[w] += subA[row + w*RTS][k] * subB[k][col];
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}
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}
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//barrier(CLK_LOCAL_MEM_FENCE);
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__syncthreads();
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}
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for (int w = 0; w < WPT; w++) {
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if (M <= (global_row + w*RTS));// C[(global_row + w*RTS)*N + global_col] = 0.0f;
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else if (N <= global_col);// C[(global_row + w*RTS)*N + global_col] = 0.0f;
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else
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C[(global_row + w*RTS)*N + global_col] = accum[w];
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}
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/*
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__shared__ float A_tile[BLOCK_DIM][BLOCK_DIM];
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__shared__ float B_tile[BLOCK_DIM][BLOCK_DIM];
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float acc_sum{0};
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for (int tile_idx{0};
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tile_idx < ceilf(static_cast<float>(K) / BLOCK_DIM); ++tile_idx)
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{
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int i{blockIdx.y * blockDim.y + threadIdx.y};
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int j{tile_idx * blockDim.x + threadIdx.x};
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if ((i < M) && (j < K))
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{
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A_tile[threadIdx.y][threadIdx.x] = A[i * K + j];
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}
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else
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{
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A_tile[threadIdx.y][threadIdx.x] = 0;
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}
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i = tile_idx * blockDim.y + threadIdx.y;
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j = blockIdx.x * blockDim.x + threadIdx.x;
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if ((i < K) && (j < N))
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{
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B_tile[threadIdx.y][threadIdx.x] = B[i * N + j];
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}
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else
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{
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B_tile[threadIdx.y][threadIdx.x] = 0;
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}
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__syncthreads();
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for (int k{0}; k < BLOCK_DIM; ++k)
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{
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acc_sum += A_tile[threadIdx.y][k] * B_tile[k][threadIdx.x];
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printf("threadidx.y %d k %d threadidx.x %d k %d \n", threadIdx.y, k, threadIdx.x, k);
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}
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__syncthreads();
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}
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// 2D block and 2D thread
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// Each thread computes one cell in C.
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int i{blockIdx.y * blockDim.y + threadIdx.y};
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int j{blockIdx.x * blockDim.x + threadIdx.x};
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if ((i < M) && (j < N))
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{
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C[i * N + j] = acc_sum;
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}
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*/
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/*
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int i = blockDim.x * blockIdx.x + threadIdx.x;
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int j = blockDim.y * blockIdx.y + threadIdx.y;
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if (i >= M || j >= N)
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return;
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C[i * N + j] = 0;
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for (int k = 0; k < K; ++k) {
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C[i * N + j] += A[i * K + k] * B[k * N + j];
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}
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*/
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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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int M_size[MAX_NUM_GPU];
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for (int i = 0; i < num_devices; i++) {
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//dim3 blockDim(1, 1, 1);
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//dim3 gridDim(Mend[i] - Mbegin[i], N, 1);
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dim3 blockDim(TS/WPT, TS, 1);
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dim3 gridDim(((Mend[i] - Mbegin[i] + TS -1)/TS), (N+TS-1)/TS,1);
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M_size[i] = Mend[i] - Mbegin[i];
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CUDA_CALL( cudaSetDevice(i) );
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sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], M_size[i], 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( 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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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]) * K * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b_d[i], K * N * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c_d[i], (Mend[i] - Mbegin[i]) * 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( 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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CUDA_CALL( cudaMemcpy(b_d[i], B, K * N * 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 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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// Download C matrix from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(C + Mbegin[i] * N, c_d[i],
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(Mend[i] - Mbegin[i]) * N * 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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