By Zhang, Zhang, Added

### Background

Intel® MKL provides the general purpose BLAS* matrix multiply routines ?GEMM defined as follows:

C := alpha*op(A)*op(B) + beta*C

where *alpha* and *beta* are scalars, *op(A)* is an *m-by-k* matrix, *op(**B)* is a *k-by-n* matrix, *C* is an *m-by-n* matrix, with *op(X) *being either *X, *or *X ^{T}, *or

*X*.

^{H}In this article, we describe a ?GEMM extension called ?HAMM that allows us to update only the upper or lower triangular part of the result matrix at performance levels close to that which ?GEMM provides. When both *C* and *op(A)*op(B)* are symmetric, we are able to exploit these symmetries by only updating the upper (or lower) triangular part of the output matrix *C*. This lower or upper triangular update requires approximately half of the floating-point computations of ?GEMM, and thus can be computed in roughly half the time of ?GEMM.

### Algorithm

A naïve ?HAMM implementation could simply be a call to ?GEMM where we ignore the upper or lower half of the computed result, but that approach would not provide the 50% performance boost sought over a single ?GEMM call. If we instead employ a recursive divide-and-conquer approach to compute just the upper or lower half of the result matrix, then we should be able to get within about 80% of ?GEMM performance. The first step of the algorithm is shown in Figure 1. First we call ?GEMM on just some of the rows and columns of *A*, *B*, and *C* to compute the rectangular submatrix *C _{1}*. In Figure 2, we do the same again for the smaller triangular parts of Figure 1 to produce

*C*

_{2 }and

*C*

_{3}. We then continue this process, recursively, until the the full upper or lower triangular result has been computed. We stop the recursion when the problem size reaches a predefined threshold, which is the base case of the recursive algorithm. We solve the base case using the naïve algorithm, which involves computing a small submatrix no bigger than the threshold size on either dimension. The brown blocks in Figure 3 shows the base case situation. Note that some spurious computation (in light brown) are done in the naïve algorithm -- we just simply discard these elements because they are not needed.

Using DHAMM as an example (where the data type is double precision floating point), the interface of DHAMM in C is:

void dhamm(char *uplo, char *transa, char *transb, MKL_INT *m, MKL_INT *n, MKL_INT *k, double *alpha, double *A, MKL_INT *lda, double *B, MKL_INT *ldb, double *beta, double *C, MKL_INT* ldc);

As a comparison, the interface of DGEMM looks like:

void dgemm(char *transa, char *transb, MKL_INT *m, MKL_INT *n, MKL_INT *k, double *alpha, double *A, MKL_INT *lda, double *B, MKL_INT *ldb, double *beta, double *C, MKL_INT* ldc);

The only difference is that DHAMM has an extra *uplo *argument in the first position of the argument list.

A sample implementation is provided for DHAMM. See the link at the bottom of this article to download the source code.

### Performance

Benchmarking on a two-socket Intel® Xeon® E5 processor shows that the recursive algorithm can get within roughly 80% of DGEMM performance, while the naïve implementation slowly approaches 50% of the performance of DGEMM.

### Notes

- If matrix
*C*was symmetric or Hermitian before calling ?HAMM, then this symmetry ispreserved during this operation, unless the product*not**op(A)*op(B)*is symmetric or Hermitian, for example, when*op(A)*op(B)*=*F*D*F*, where^{T}*D*is a diagonal matrix and*F*is a general matrix. - The default value for
- The sample implementation source code is only meant to be used as an example. It is not rigorously tested and does not necessarily exhibit all elements of good programming practice. Users should build their own solutions based on it.

## Comments (1)

TopVineet Y. said on

Hi

Zhang

I wrote about the absence of a routine in MKL in this forum from which we can calculate only the upper or lower part of matrix multiplication and in response to which I received dhamm routine in 2012. I am presently working on a paper to demonstrate the best way to do matrix multiplication for multidimensional separable Quadratic forms and would be presenting my research in Georgia in a conference on society for industrial and applied mathematics in 2014. I want to use your routine as part of my presentation. Let me know if Intel or you have any issues with it. If you want we can provide you co-authorship on a paper that we plan to write by April next year which would also include description about routines for doing sparse-sparse matrix multipication for quadratic forms

Vineet

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