LAPACK's user's guide
ScaLAPACK user's guide
MPI: The Complete Reference
An updated set of basic linear algebra subprograms (BLAS)
ACM Transactions on Mathematical Software (TOMS)
Geophysical data analysis using Python
Computers & Geosciences
Self-adapting software for numerical linear algebra and LAPACK for clusters
Parallel Computing - Special issue: Parallel and distributed scientific and engineering computing
Parallel netCDF: A High-Performance Scientific I/O Interface
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Python Scripting for Computational Science (Texts in Computational Science and Engineering)
Python Scripting for Computational Science (Texts in Computational Science and Engineering)
Making a Supercomputer Do What You Want: High-Level Tools for Parallel Programming
Computing in Science and Engineering
VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
UPCBLAS: a library for parallel matrix computations in Unified Parallel C
Concurrency and Computation: Practice & Experience
Hi-index | 0.00 |
In many high performance engineering and scientific applications there is a need to use parallel software libraries. Researchers behind these applications find it difficult to understand the interfaces to these libraries because they carry arguments that are related to the parallel environment and performance in addition to arguments related to the problem at hand. In this paper we introduce the use of high level user interfaces for ScaLAPACK. Concretely, a Python-based interface to ScaLAPACK is proposed. Numerical experiments comparing traditional programming practices with our proposed approach are presented. These experiments evaluate not only the performance of the Python interfaces but also how user friendlier they are, compared to the original calls, and show that PyScaLAPACK does not hinder the performance deliverance of ScaLAPACK. Finally, an example of a real scientific application code, whose functionality can be prototyped or extended with the use of PyScaLAPACK, is presented.