10. Kernel Optimization

Performance optimization for custom quantization kernels requires systematic profiling and iterative improvement. The goal is achieving peak throughput while maintaining numerical accuracy across quantized weight distributions.

Occupancy and Thread Block Design

Thread block occupancy directly impacts GPU utilization. Calculate theoretical occupancy using shared memory and register constraints:

__global__ void quantized_gemm_kernel(
    const int8_t* __restrict__ A,
    const int8_t* __restrict__ B,
    float* C, int M, int N, int K,
    const float* scale_a, const float* scale_b,
    float output_scale, int vec_len) {
    
    __shared__ float shA[128][4];
    __shared__ float shB[128][4];
    
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    int block_x = blockIdx.x;
    int block_y = blockIdx.y;
    
    float acc[4][4] = {0.0f};
    
    for (int kb = 0; kb < (K + vec_len - 1) / vec_len; kb++) {
        // Cooperative load into shared memory
        int base_k = kb * vec_len;
        load_vector_gemm(shA, shB, A, B, block_x, block_y,
                         tx, ty, base_k, vec_len, K);
        __syncthreads();
        
        // Compute partial result
        for (int k = 0; k < vec_len; k++) {
            for (int i = 0; i < 4; i++) {
                for (int j = 0; j < 4; j++) {
                    acc[i][j] += shA[tx * 4 + i][k] * shB[ty * 4 + j][k];
                }
            }
        }
        __syncthreads();
    }
    
    // Write output with scaling
    write_output(C, acc, block_x, block_y, tx, ty, M, N, 
                 scale_a, scale_b, output_scale);
}

Instruction-Level Optimization

Loop unrolling reduces pipeline stalls. Manually unroll inner loops when trip counts are known or bounded:

#pragma unroll 8
for (int i = 0; i < 8; i++) {
    int idx = base_idx + i;
    if (idx < K) {
        int8_t a_val = ldg(&A[idx * K + row_a]);
        int8_t b_val = ldg(&B[col_b * K + idx]);
        accumulator += convert_to_float(a_val) * convert_to_float(b_val);
    }
}

Warp-level primitives enable efficient reductions and data movement without synchronization barriers.