Sample HIP Program
#include "hip/hip_runtime.h"
/*
* A "hipified" version of the NVIDIA CUDA matrix multiple example
*/
#include <hip/hip_runtime.h>
#include <stdio.h>
// Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.stride + col)
typedef struct {
int width; /* number of columns */
int height; /* number of rows */
int stride;
float* elements;
} Matrix;
// Get a matrix element
/*__forceinline__*/ __device__ float GetElement(const Matrix A, int row, int col)
{
return A.elements[row * A.stride + col];
}
// Set a matrix element
__forceinline__ __device__ void SetElement(Matrix A, int row, int col, float value)
{
A.elements[row * A.stride + col] = value;
}
// Thread block size
#define BLOCK_SIZE 2
// Get the BLOCK_SIZExBLOCK_SIZE sub-matrix Asub of A that is
// located col sub-matrices to the right and row sub-matrices down
// from the upper-left corner of A
/*__forceinline__*/ __device__ Matrix GetSubMatrix(Matrix A, int row, int col)
{
Matrix Asub;
Asub.width = BLOCK_SIZE;
Asub.height = BLOCK_SIZE;
Asub.stride = A.stride;
Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row
+ BLOCK_SIZE * col];
return Asub;
}
// Forward declaration of the matrix multiplication kernel
__global__ void MatMulKernel(const Matrix, const Matrix, Matrix);
// Matrix multiplication - Host code
// Matrix dimensions are assumed to be multiples of BLOCK_SIZE
void MatMul(const Matrix A, const Matrix B, Matrix C)
{
// Load A and B to device memory
Matrix d_A;
d_A.width = d_A.stride = A.width; d_A.height = A.height;
size_t size = A.width * A.height * sizeof(float);
hipMalloc((void**)&d_A.elements, size);
hipMemcpy(d_A.elements, A.elements, size,
hipMemcpyHostToDevice);
Matrix d_B;
d_B.width = d_B.stride = B.width; d_B.height = B.height;
size = B.width * B.height * sizeof(float);
hipMalloc((void**)&d_B.elements, size);
hipMemcpy(d_B.elements, B.elements, size,
hipMemcpyHostToDevice);
// Allocate C in device memory
Matrix d_C;
d_C.width = d_C.stride = C.width; d_C.height = C.height;
size = C.width * C.height * sizeof(float);
hipMalloc((void**)&d_C.elements, size);
// Invoke kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
hipLaunchKernelGGL(MatMulKernel, dim3(dimGrid), dim3(dimBlock), 0, 0, d_A, d_B, d_C);
// Read C from device memory
hipMemcpy(C.elements, d_C.elements, size,
hipMemcpyDeviceToHost);
// Free device memory
hipFree(d_A.elements);
hipFree(d_B.elements);
hipFree(d_C.elements);
}
// Matrix multiplication kernel called by MatrixMul()
__global__ void MatMulKernel(Matrix A, Matrix B, Matrix C)
{
// Block row and column
int blockRow = blockIdx.y;
int blockCol = blockIdx.x;
// Each thread block computes one sub-matrix Csub of C
Matrix Csub = GetSubMatrix(C, blockRow, blockCol); /* STOP(called-subroutine): */
// Each thread computes one element of Csub
// by accumulating results into Cvalue
float Cvalue = 0; /* MARKER(plant-after-libload): IN_KERNEL_LINE */
// Thread row and column within Csub
int row = threadIdx.y;
int col = threadIdx.x;
// Loop over all the sub-matrices of A and B that are
// required to compute Csub
// Multiply each pair of sub-matrices together
// and accumulate the results
for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) {
// Get sub-matrix Asub of A
Matrix Asub = GetSubMatrix(A, blockRow, m);
// Get sub-matrix Bsub of B
Matrix Bsub = GetSubMatrix(B, m, blockCol);
// Shared memory used to store Asub and Bsub respectively
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
// Load Asub and Bsub from device memory to shared memory
// Each thread loads one element of each sub-matrix
As[row][col] = GetElement(Asub, row, col);
Bs[row][col] = GetElement(Bsub, row, col);
// Synchronize to make sure the sub-matrices are loaded
// before starting the computation
__syncthreads();
// Multiply Asub and Bsub together
for (int e = 0; e < BLOCK_SIZE; ++e)
Cvalue += As[row][e] * Bs[e][col];
// 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 Csub to device memory
// Each thread writes one element
SetElement(Csub, row, col, Cvalue); /* STOP(inlined-subroutine): */
// Just a place to set a breakpoint in the debugger
__syncthreads();
__syncthreads(); /* STOP: Csub should be fully updated */
}
// A function to serve as a backstop for setting breakpoints in the
// preceding kernel, so that they don't slop over to the next function.
static int breakpoint_backstop() { return 0; }
static Matrix
cons_Matrix (int width_, int height_)
{
Matrix A;
A.width = width_;
A.height = height_;
A.stride = width_;
A.elements = (float*) malloc(sizeof(*A.elements) * width_ * height_);
for (int row = 0; row < height_; row++)
for (int col = 0; col < width_; col++)
A.elements[row * width_ + col] = row * 10.0 + col;
return A;
}
static void
print_Matrix (Matrix A, const char *name)
{
printf("%s:\n", name);
for (int row = 0; row < A.height; row++)
for (int col = 0; col < A.width; col++)
printf ("[%5d][%5d] %f\n", row, col, A.elements[row * A.stride + col]);
}
// Multiply an m*n matrix with an n*p matrix results in an m*p matrix.
// Usage: tx_cuda_matmul [ m [ n [ p ] ] ]
// m, n, and p default to 1, and are multiplied by BLOCK_SIZE.
int main(int argc, char **argv)
{
// hipSetDevice(0);
const int m = BLOCK_SIZE * (argc > 1 ? atoi(argv[1]) : 1);
const int n = BLOCK_SIZE * (argc > 2 ? atoi(argv[2]) : 1);
const int p = BLOCK_SIZE * (argc > 3 ? atoi(argv[3]) : 1);
Matrix A = cons_Matrix(m, n); /* MARKER(plant-after-libload): IN_MAIN_LINE */
Matrix B = cons_Matrix(n, p);
Matrix C = cons_Matrix(m, p);
MatMul(A, B, C);
print_Matrix(A, "A");
print_Matrix(B, "B");
print_Matrix(C, "C");
return breakpoint_backstop();
}
