We present two implementations of dense matrix multiplication based on two different non-
canonical array layouts: one based on a hypermatrix data structure (HM) where data …

This paper shows how a sparse hypermatrix Cholesky factorization can be improved. This is
accomplished by means of efficient codes which operate on very small dense matrices …

Matrix computations lie at the heart of most scientific computational tasks. The solution of
linear systems of equations is a very frequent operation in many fields in science …

The use of highly optimized inner kernels is of paramount importance for obtaining efficient
numerical algorithms. Often, such kernels are created by hand. In this paper, however, we …

Dense linear algebra codes are often expressed and coded in terms of BLAS calls. This
approach, however, achieves suboptimal performance due to the overheads associated to …

LNCS 3911 - Adapting Linear Algebra Codes to the Memory Hierarchy Using a Hypermatrix
Scheme Page 1 Adapting Linear Algebra Codes to the Memory Hierarchy Using a …

Efficient execution of numerical algorithms requires adapting the code to the underlying
execution platform. In this paper we show the process of fine tuning our sparse Hypermatrix …

In this paper we present our work on the the parallelization of a matrix multiplication code
based on the hypermatrix data structure. We have used OpenMP for the parallelization. We …

Efficient execution on processors with multiple cores requires the exploitation of parallelism
within the processor. For many dense linear algebra codes this, in turn, requires the efficient …

Using nonlinear array layouts in dense matrix operations Page 1 Using nonlinear array
layouts in dense matrix operations JR Herrero Introduction: A bottom-up approach …