Here's an example of a custom HIP kernel for matrix multiplication that can leverage Matrix Cores:
```cpp
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <iostream>
// Define matrix dimensions
#define M 16
#define N 16
#define K 16
// HIP kernel for matrix multiplication
__global__ void matrixMulKernel(half* A, half* B, float* C) {
// Shared memory for tile of A and B
__shared__ half As[M][K];
__shared__ half Bs[K][N];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
// Index of the first sub-matrix of A processed by the block
int aBegin = K * M * by;
// Index of the last sub-matrix of A processed by the block
int aEnd = aBegin + K - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = M;
// Index of the first sub-matrix of B processed by the block
int bBegin = N * bx;
// Step size used to iterate through the sub-matrices of B
int bStep = K * N;
// Csub is used to store the element of the block sub-matrix
// that is computed by the thread
float Csub = 0;
// Loop over all the sub-matrices of A and B
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
// Load the matrices from device memory to shared memory
As[ty][tx] = A[a + K * ty + tx];
Bs[ty][tx] = B[b + N * ty + tx];
// Synchronize to make sure the matrices are loaded
__syncthreads();
// Multiply the two matrices
#pragma unroll
for (int k = 0; k < K; ++k) {
Csub += __half2float(As[ty][k]) * __half2float(Bs[k][tx]);
}
// Synchronize to make sure that the preceding
// computation is done before loading two new
// sub-matrices of A and B in the next iteration
__syncthreads();
}
// Write the block sub-matrix to device memory
// each thread writes one element
int c = N * M * by + M * bx;
C[c + N * ty + tx] = Csub;
}
// Host function to launch the kernel
void launchMatrixMulKernel(half* A, half* B, float* C, int numBlocks) {
dim3 threadsPerBlock(M, N);
dim3 blocksPerGrid(numBlocks, numBlocks);
hipLaunchKernelGGL(matrixMulKernel, blocksPerGrid, threadsPerBlock, 0, 0, A, B, C);
}
// Error checking macro
#define HIP_CHECK(call) { hipError_t err = call; if (err != hipSuccess) { std::cerr << "HIP error: " << hipGetErrorString(err) << std::endl; exit(1); } }
int main() {
// Allocate memory
half *A, *B;
float *C;
HIP_CHECK(hipMalloc(&A, M * K * sizeof(half)));
HIP_CHECK(hipMalloc(&B, K * N * sizeof(half)));
HIP_CHECK(hipMalloc(&C, M * N * sizeof(float)));
// Initialize matrices (you would typically do this on the GPU)
// ...
// Launch kernel
launchMatrixMulKernel(A, B, C, 1); // Assuming 1 block for simplicity
// Clean up
HIP_CHECK(hipFree(A));
HIP_CHECK(hipFree(B));
HIP_CHECK(hipFree(C));
return 0;
}
```
Key points about this example:
1. It uses `half` precision for input matrices A and B, which can potentially benefit from Matrix Core acceleration.
2. The kernel is designed for 16x16 matrices, which is a common size for Matrix Core operations.
3. Shared memory is used to improve performance by reducing global memory accesses.
4. The main computation loop uses `__half2float` conversions. On GPUs with native FP16 support, these conversions might be optimized out.
5. The kernel uses a tiled approach, which is generally efficient for matrix multiplication.
6. Error checking is included for HIP calls.
Important considerations:
1. This kernel doesn't guarantee the use of Matrix Cores. The actual use of Matrix Cores depends on the GPU architecture and the HIP compiler's optimizations.
2. For larger matrices, you'd need to implement a more sophisticated tiling strategy.
3. Performance tuning is crucial. You might need to experiment with different block sizes and memory access patterns for optimal performance.
4. The HIP runtime and compiler will attempt to optimize this code for the target GPU, potentially leveraging Matrix Cores if available.
5. For production use, you should implement proper error handling and potentially use more sophisticated synchronization methods.
To fully leverage Matrix Cores, you might need to use specific intrinsics or rely on compiler optimizations. The exact method can vary depending on the GPU architecture and HIP version. Always profile your code to ensure you're getting the expected performance benefits.
