Warp shuffle instruction not working as expected

I tried a simple experiment based on my previous comments: the low-hanging fruit is to try using warp shuffle in place of the A shared tile. I used the code presented in the programming guide as a starting point, selecting 32x32 tiles. Interestingly the performance is noticeably slower on a L4 GPU with CUDA 12.2.1 for reasonably large matrix sizes (4096x4096).

Here’s what I came up with:

# cat t15.cu
#include <iostream>
#include <cstdlib>
#include <time.h>
#include <sys/time.h>
#define USECPSEC 1000000ULL

unsigned long long dtime_usec(unsigned long long start=0){

  timeval tv;
  gettimeofday(&tv, 0);
  return ((tv.tv_sec*USECPSEC)+tv.tv_usec)-start;
}
#define RNG 3
// Thread block size
#define BLOCK_SIZE 32
// Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.stride + col)
typedef struct {
    int width;
    int height;
    int stride;
    float* elements;
} Matrix;
// Get a matrix element
__device__ float GetElement(const Matrix A, int row, int col)
{
    return A.elements[row * A.stride + col];
}
// Set a matrix element
__device__ void SetElement(Matrix A, int row, int col,
                           float value)
{
    A.elements[row * A.stride + col] = value;
}
// 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
 __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;
}

// Matrix multiplication kernel called by MatMul()
 __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);
    // Each thread computes one element of Csub
    // by accumulating results into Cvalue
    float Cvalue = 0;
    // 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 Bs[BLOCK_SIZE][BLOCK_SIZE];
        __shared__ float As[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);
}


 __global__ void MatMulKernel_shflA(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);
    // Each thread computes one element of Csub
    // by accumulating results into Cvalue
    float Cvalue = 0;
    // 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 Bs[BLOCK_SIZE][BLOCK_SIZE];
        float my_A = 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 += __shfl_sync(0xFFFFFFFF, my_A, 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);
}

// 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);
    cudaMalloc(&d_A.elements, size);
    cudaMemcpy(d_A.elements, A.elements, size,
               cudaMemcpyHostToDevice);
    Matrix d_B;
    d_B.width = d_B.stride = B.width; d_B.height = B.height;
    size = B.width * B.height * sizeof(float);
    cudaMalloc(&d_B.elements, size);
    cudaMemcpy(d_B.elements, B.elements, size,
    cudaMemcpyHostToDevice);
    Matrix h_C_shfl;
    h_C_shfl.width = h_C_shfl.stride = C.width; h_C_shfl.height = C.height;
    h_C_shfl.elements = new float[h_C_shfl.width*h_C_shfl.height];
    // 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);
    cudaMalloc(&d_C.elements, size);
    // Invoke kernel
    dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
    dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
    MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C); // warm-up
    cudaDeviceSynchronize();
    unsigned long long dt = dtime_usec(0);
    MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
    cudaDeviceSynchronize();
    dt = dtime_usec(dt);
    std::cout << "shared kernel time:  " << dt << "us" << std::endl;
    cudaMemcpy(C.elements, d_C.elements, size, cudaMemcpyDeviceToHost);
    MatMulKernel_shflA<<<dimGrid, dimBlock>>>(d_A, d_B, d_C); // warm-up
    cudaDeviceSynchronize();
    dt = dtime_usec(0);
    MatMulKernel_shflA<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
    cudaDeviceSynchronize();
    dt = dtime_usec(dt);
    std::cout << "shuffle kernel time: " << dt << "us" << std::endl;
    // Read C from device memory
    cudaMemcpy(h_C_shfl.elements, d_C.elements, size, cudaMemcpyDeviceToHost);
    for (int i = 0; i < h_C_shfl.width*h_C_shfl.height; i++) if (h_C_shfl.elements[i] != C.elements[i]) {std::cout << "mismatch at: " << i << " should be: " << C.elements[i] << " was: " << h_C_shfl.elements[i] << std::endl; return;}
    // Free device memory
    cudaFree(d_A.elements);
    cudaFree(d_B.elements);
    cudaFree(d_C.elements);
}

int main(){

  Matrix h_A, h_B, h_C;
  const int dim = 128*BLOCK_SIZE;
  h_A.width=h_A.stride=h_A.height=h_B.width=h_B.stride=h_B.height=h_C.width=h_C.stride=h_C.height=dim;
  h_A.elements = new float[dim*dim];
  h_B.elements = new float[dim*dim];
  h_C.elements = new float[dim*dim];
  for (int i = 0; i < dim*dim; i++) {
    h_A.elements[i] = rand()%RNG;
    h_B.elements[i] = rand()%RNG;}
  MatMul(h_A, h_B, h_C);
}
# nvcc -o t15 t15.cu -arch=sm_89
# ./t15
shared kernel time:  69830us
shuffle kernel time: 94572us
#

So this is only 1 datapoint, but the initial conclusion I have is that warp shuffle for this case doesn’t seem to be performance advantageous vs. using shared memory. I would encourage all readers not to draw sweeping conclusions from this one datapoint: there are certainly examples where refactoring a shared memory code to use warp shuffle can result in improved performance.