Best block size value for block matrix matrix multiplication

问题

I want to do block matrix-matrix multiplication with the following C code.In this approach, blocks of size BLOCK_SIZE is loaded into the fastest cache in order to reduce memory traffic during calculation.

void bMMikj(double **A , double **B , double ** C , int m, int n , int p , int BLOCK_SIZE){

   int i, j , jj, k , kk ;
   register double jjTempMin = 0.0 , kkTempMin = 0.0;

   for (jj=0; jj<n; jj+= BLOCK_SIZE) {
       jjTempMin = min(jj+ BLOCK_SIZE,n); 
       for (kk=0; kk<n; kk+= BLOCK_SIZE) {
           kkTempMin = min(kk+ BLOCK_SIZE,n); 
           for (i=0; i<n; i++) {
               for (k = kk ; k < kkTempMin ; k++) {
                   for (j=jj; j < jjTempMin; j++) {
                      C[i][j]  +=  A[i][k] * B[k][j];
                   }
               }
           }
      }
   }
}

I searched about the best suitable value of BLOCK_SIZE and I found that BLOCK_SIZE <= sqrt( M_fast / 3 ) and M_fast here is L1 cache.

In my computer, I have two L1 cache as shown here with lstopo tool. Below, I am using heuristics like starting with a BLOCK_SIZE of 4 and increasing the value by 8 for 1000 times, with different values of matrix sizes.

Hopping to get the best MFLOPS ( or the least time for multiplication ) value and the corresponding BLOCK_SIZE value will be the best suitable value.

This is the code for testing:

int BLOCK_SIZE = 4;
int m , n , p;
m = n = p = 1024; /* This value is also changed
                     and all the matrices are square, for simplicity
                     */
for(int i=0;i< 1000; i++ , BLOCK_SIZE += 8) {
    # aClock.start();
    test_bMMikj(A , B ,  C , loc_n , loc_n , loc_n ,BLOCK_SIZE);
    # aClock.stop();
}

Testing gives me different values for each matrix size and do not agree with the formula.The computer model is 'Intel® Core™ i5-3320M CPU @ 2.60GHz × 4 'with 3.8GiB and here is the Intel specification

Another question :
If I have two L1 caches, like what I have in here, should I consider BLOCK_SIZE with respect to one of them or the summation of both?

回答1:

1. Block Matrix Multiplication: The idea is to make maximum use of both temporal and spatial locality by reusing the data block currently stored in cache. Your code for the same is incorrect as it contains only 5 loops; for block there should be 6, something like :

for(int ii=0; ii<N; ii+=stride)
{
    for(int jj=0; jj<N; jj+=stride)
    {
        for(int kk=0; kk<N; kk+=stride)
        {
            for(int i=ii; i<ii+stride; ++i)
            {
                for(int j=jj; j<jj+stride; ++j)
                {
                    for(int k=kk; k<kk+stride; ++k) C[i][j] += A[i][k]*B[k][j];
                }
            }               
        }
    }
}

Initially keep both N and stride as powers of 2 for simplicity. The ijk pattern is not the most optimal, you should either go for kij or ikj, details on that here. Different access patterns have different performances, you should try all permutations of ijk.

2. Block/Stride size: It is generally said that your fastest cache (L1) should be able to accomodate 3 blocks(stride*stride) of data for optimal performance in case of matrix multiplication, but it's always good to experiment and find it for yourself. Increasing stride by 8 may not be a good idea, try to keep it as increasing powers of 2 because most block sizes are sized in that manner. And you should only look at data cache(L1d) which in your case is 32KB.

来源：https://stackoverflow.com/questions/47685422/best-block-size-value-for-block-matrix-matrix-multiplication