228 lines
6.1 KiB
Plaintext
228 lines
6.1 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 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; // ORG
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int num_devices = 4; // DEBUG
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/*
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// ORIGINAL VERSION
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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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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// 41490 GFLOPS: TS(64), WPT(8)
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#define TS 64
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#define WPT 8
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// 35000 GFLOPS: TS(72), WPT(8)
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//#define TS 72
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//#define WPT 8
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// 30490 GFLOPS: TS(64), WPT(16)
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//#define TS 64
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//#define WPT 16
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// 30000 GFLOPS: TS(64), WPT(4)
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//#define TS 64
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//#define WPT 4
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// 28000 GFLOPS: TS(72), WPT(16)
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//#define TS 72
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//#define WPT 16
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// 17000 GFLOPS: TS(16), WPT(8)
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//#define TS 16
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//#define WPT 8
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#define RTS TS/WPT
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// VERSION: PARTITIONED FOR M
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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int bx = blockIdx.x;
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int by = blockIdx.y;
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int tx = threadIdx.x;
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int ty = threadIdx.y;
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int row = threadIdx.y;
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int col = threadIdx.x;
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//int global_row = (blockDim.y*WPT)*blockIdx.y + threadIdx.y;
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//int global_col = (blockDim.x)*blockIdx.x + threadIdx.x;
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int global_row = (blockDim.y*WPT)*by + ty;
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int global_col = (blockDim.x)*bx + tx;
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__shared__ float Asub[TS][TS];
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__shared__ float Bsub[TS][TS];
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float intermediate_val[WPT];
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// printf("DEBUG: (bx,by) = (%d, %d), (tx,ty)=(%d, %d), (bsx,bsy)=(%d,%d), (global_row, global_col)= (%d,%d) \n",bx, by, tx,ty, blockDim.x, blockDim.y, global_row, global_col);
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for(int w=0; w < WPT; w++) {
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intermediate_val[w] = 0.0f;
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}
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const int num_tiles = (K % TS) ? K/TS +1 : K/TS; // aligned for TS(tile size)
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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 t_row = TS * t + row;
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const int t_col = TS * t + col;
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// for zero padding for aligned area
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if( (global_row+w*RTS >= M) || (t_col >= K) ) Asub[row+w*RTS][col] = 0.0f;
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else Asub[row+w*RTS][col] = A[(global_row + w*RTS) * K + t_col];
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// for zero padding for aligned area
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if( (t_row + w*RTS >= K) || (global_col >= N) ) Bsub[row+w*RTS][col] = 0.0f;
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else Bsub[row+w*RTS][col] = B[(t_row + w*RTS) * N + global_col];
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}
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__syncthreads();
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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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intermediate_val[w] += Asub[row+w*RTS][k] * Bsub[k][col];
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}
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}
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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( (global_row+w*RTS >= M) || (global_col >= N) ) continue;
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else C[(global_row + w*RTS)*N + global_col] = intermediate_val[w];
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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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//static int Mi[MAX_NUM_GPU];
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static int Msz;
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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 bsx, bsy;
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bsx = TS;
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bsy = TS/WPT;
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dim3 blockDim(bsx, bsy, 1);
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Msz = (Mend[i] - Mbegin[i]);
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int gx, gy;
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gx = N;
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gy = (Msz+WPT-1)/WPT;
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// aligned for block
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gx = (gx + bsx -1)/bsx;
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gy = (gy + bsy -1)/bsy;
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dim3 gridDim(gx, gy, 1);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Msz, 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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