matmul:cuda

excuter/op-mem-cuda/src/deepx/tensorfunc/matmul.cu

@@ -0,0 +1,78 @@
+#include "deepx/tensorfunc/cuda.hpp"
+
+#include <cuda_fp64.h>
+#include <cuda_fp32.h>
+#include "deepx/tensor.hpp"
+
+#include "deepx/tensorfunc/matmul.hpp"
+#include "deepx/tensorfunc/authors.hpp"
+#include "deepx/tensorfunc/cuda.hpp"
+
+namespace deepx::tensorfunc {
+
+  #define BLOCK_SIZE 32
+
+  __global__ void fp64MatmulKernel(double *C, const double *A, const double *B, 
+                                   int M, int N, int K) {
+      // 定义共享内存块，用于缓存A和B的矩阵块
+      __shared__ double tileA[BLOCK_SIZE][BLOCK_SIZE];
+      __shared__ double tileB[BLOCK_SIZE][BLOCK_SIZE];
+
+      // 计算当前线程处理的全局矩阵位置
+      int row = blockIdx.y * BLOCK_SIZE + threadIdx.y;
+      int col = blockIdx.x * BLOCK_SIZE + threadIdx.x;
+
+      double sum = 0.0;
+
+      // 分块循环处理整个K维度
+      for (int t = 0; t < (K + BLOCK_SIZE - 1) / BLOCK_SIZE; ++t) {
+          // 计算当前块的起始位置
+          int tiledK = t * BLOCK_SIZE;
+
+          // 加载A的块到共享内存（行优先）
+          int loadA_col = tiledK + threadIdx.x;
+          if (row < M && loadA_col < K) {
+              tileA[threadIdx.y][threadIdx.x] = A[row * K + loadA_col];
+          } else {
+              tileA[threadIdx.y][threadIdx.x] = 0.0;  // 填充0处理边界
+          }
+
+          // 加载B的块到共享内存（列优先等效处理）
+          int loadB_row = tiledK + threadIdx.y;
+          if (col < N && loadB_row < K) {
+              tileB[threadIdx.y][threadIdx.x] = B[loadB_row * N + col];
+          } else {
+              tileB[threadIdx.y][threadIdx.x] = 0.0;  // 填充0处理边界
+          }
+
+          __syncthreads();  // 确保块加载完成
+
+          // 计算当前块的矩阵乘法贡献
+          for (int k = 0; k < BLOCK_SIZE; ++k) {
+              sum += tileA[threadIdx.y][k] * tileB[k][threadIdx.x];
+          }
+
+          __syncthreads();  // 确保计算完成再加载下一块
+      }
+
+      // 只将有效范围内的结果写入全局内存
+      if (row < M && col < N) {
+          C[row * N + col] = sum;
+      }
+  }
+
+  // 主机函数调用内核
+  void fp64Matmul(double *d_C, const double *d_A, const double *d_B, 
+                  int M, int N, int K) {
+      dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
+      dim3 dimGrid((N + BLOCK_SIZE - 1) / BLOCK_SIZE, 
+                   (M + BLOCK_SIZE - 1) / BLOCK_SIZE);
+
+      fp64MatmulKernel<<<dimGrid, dimBlock>>>(d_C, d_A, d_B, M, N, K);
+  }
+
+
+}
+
+} // namespace tensorfunc
+} // namespace deepx