使用内在函数向量化矩阵乘法的加法部分？

Question

I'm trying to vectorize matrix multiplication using blocking and vector intrinsics. It seems to me that the addition part in the vector multiplication cannot be vectorized. Could you please see if I can improve my code to vectorize further?

    double dd[4], bb[4];
    __m256d op_a, op_b, op_d;
    for(i = 0; i < num_blocks; i++){
        for(j = 0; j < num_blocks; j++){
            for(k = 0; k < num_blocks; k++){
                for(ii = 0; ii < block_size ; ii++){
                    for(kk = 0; kk < block_size; kk++){
                        for(jj = 0; jj < block_size ; jj+=4){

                            aoffset=n*(i*block_size+ii)+j*block_size +jj ;
                            boffset=n*(j*block_size+jj)+k*block_size +kk;
                            coffset=n*(i*block_size+ii)+ k*block_size + kk;

                            bb[0]=b[n*(j*block_size+jj)+k*block_size +kk];
                            bb[1]=b[n*(j*block_size+jj+1)+k*block_size +kk];
                            bb[2]=b[n*(j*block_size+jj+2)+k*block_size +kk];
                            bb[3]=b[n*(j*block_size+jj+3)+k*block_size +kk];

                            op_a = _mm256_loadu_pd (a+aoffset);
                            op_b= _mm256_loadu_pd (bb);
                            op_d = _mm256_mul_pd(op_a, op_b);
                            _mm256_storeu_pd (dd, op_d);
                            c[coffset]+=(dd[0]+dd[1]+dd[2]+dd[3]);

                        }
                    }
                }
            }
        }
    }

Thanks.

Answer 1

You can use this version of matrix multiplication (c[i,j] = a[i,k]*b[k,j]) algorithm (scalar version):

for(int i = 0; i < i_size; ++i)
{
    for(int j = 0; j < j_size; ++j)
         c[i][j] = 0;

    for(int k = 0; k < k_size; ++k)
    {
         double aa = a[i][k];
         for(int j = 0; j < j_size; ++j)
             c[i][j] += aa*b[k][j];
    }
}

And vectorized version:

for(int i = 0; i < i_size; ++i)
{
    for(int j = 0; j < j_size; j += 4)
         _mm256_store_pd(c[i] + j, _mm256_setzero_pd());

    for(int k = 0; k < k_size; ++k)
    {
         __m256d aa = _mm256_set1_pd(a[i][k]);
         for(int j = 0; j < j_size; j += 4)
         {
             _mm256_store_pd(c[i] + j, _mm256_add_pd(_mm256_load_pd(c[i] + j), _mm256_mul_pd(aa, _mm256_load_pd(b[k] + j))));
         }
    }
}

Answer 2

"Horizontal add" is a more recent edition to the SSE instruction set, so you can't use the accelerated version if compatibility with many different processors is your goal.

However, you definitely can vectorize the additions. Note that the inner loop only affects a single coffset . You should move the coffset calculation outward (the compiler will do this automatically, but the code is more readable if you do) and also use four accumulators in the innermost loop, performing the horizontal add only once per coffset . This is an improvement even if vector horizontal add is used, and for the scalar horizontal add, it's pretty big.

Something like:

for(kk = 0; kk < block_size; kk++){
    op_e = _mm256_setzero_pd();

    for(jj = 0; jj < block_size ; jj+=4){
        aoffset=n*(i*block_size+ii)+j*block_size +jj ;
        boffset=n*(j*block_size+jj)+k*block_size +kk;

        bb[0]=b[n*(j*block_size+jj)+k*block_size +kk];
        bb[1]=b[n*(j*block_size+jj+1)+k*block_size +kk];
        bb[2]=b[n*(j*block_size+jj+2)+k*block_size +kk];
        bb[3]=b[n*(j*block_size+jj+3)+k*block_size +kk];

        op_a = _mm256_loadu_pd (a+aoffset);
        op_b= _mm256_loadu_pd (bb);
        op_d = _mm256_mul_pd(op_a, op_b);
        op_e = _mm256_add_pd(op_e, op_d);
    }
    _mm256_storeu_pd(dd, op_e);
    coffset = n*(i*block_size+ii)+ k*block_size + kk;
    c[coffset] = (dd[0]+dd[1]+dd[2]+dd[3]);
}

You can also speed this up by doing a transpose on b beforehand, instead of gathering the vector inside the innermost loop.