Abstract. 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 such calls. Taking as an example the dense Cholesky factorization of a symmetric positive definite matrix we show that the potential of non-canonical data structures for dense linear algebra can be better exploited with the use of specialized inner kernels. The use of non-canonical data structures together with specialized inner kernels has low overhead and can produce excellent performance
This paper discusses optimizing computational linear algebra algorithms on a ring cluster of IBM R...
Design by Transformation (DxT) is an approach to software development that encodes domain-specific p...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
The goal of the LAPACK project is to provide efficient and portable software for dense numerical lin...
Matrix computations lie at the heart of most scientific computational tasks. The solution of linear ...
Abstract. The use of highly optimized inner kernels is of paramount im-portance for obtaining effici...
In this article we present a systematic approach to the derivation of families of high-performance a...
The design of compact data structures for representing the structure of the Cholesky factor L of a s...
<p>Scientific Computation provides a critical role in the scientific process because it allows us as...
Recursion leads to automatic variable blocking for dense linear‐algebra algorithms. The recursive wa...
We discuss the interface design for the Sparse Basic Linear Algebra Subprograms (BLAS), the kernels ...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
One of the greatest efforts of computational scientists is to translate the mathematical model descr...
A technique for optimizing software is proposed that involves the use of a standardized set of compu...
This work is comprised of two different projects in numerical linear algebra. The first project is a...
This paper discusses optimizing computational linear algebra algorithms on a ring cluster of IBM R...
Design by Transformation (DxT) is an approach to software development that encodes domain-specific p...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
The goal of the LAPACK project is to provide efficient and portable software for dense numerical lin...
Matrix computations lie at the heart of most scientific computational tasks. The solution of linear ...
Abstract. The use of highly optimized inner kernels is of paramount im-portance for obtaining effici...
In this article we present a systematic approach to the derivation of families of high-performance a...
The design of compact data structures for representing the structure of the Cholesky factor L of a s...
<p>Scientific Computation provides a critical role in the scientific process because it allows us as...
Recursion leads to automatic variable blocking for dense linear‐algebra algorithms. The recursive wa...
We discuss the interface design for the Sparse Basic Linear Algebra Subprograms (BLAS), the kernels ...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
One of the greatest efforts of computational scientists is to translate the mathematical model descr...
A technique for optimizing software is proposed that involves the use of a standardized set of compu...
This work is comprised of two different projects in numerical linear algebra. The first project is a...
This paper discusses optimizing computational linear algebra algorithms on a ring cluster of IBM R...
Design by Transformation (DxT) is an approach to software development that encodes domain-specific p...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...