Use of Computational Kernels in Full and Sparse Linear Solvers, Efficient Code Design on High-Performance RISC Processors (1997)
| Venue: | RISC processors, inVector and Parallel Processing { VECPAR'96 |
| Citations: | 2 - 0 self |
BibTeX
@INPROCEEDINGS{Daydé97useof,
author = {Michel J. Daydé and Iain S. Duff},
title = {Use of Computational Kernels in Full and Sparse Linear Solvers, Efficient Code Design on High-Performance RISC Processors},
booktitle = {RISC processors, inVector and Parallel Processing { VECPAR'96},
year = {1997},
pages = {108--139},
publisher = {Springer}
}
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Abstract
. We believe that the availability of portable and efficient serial and parallel numerical libraries that can be used as building blocks is extremely important for both simplifying application software development and improving reliability. This is illustrated by considering the solution of full and sparse linear systems. We describe successive layers of computational kernels such as the BLAS, the sparse BLAS, blocked algorithms for factorizing full systems, direct and iterative methods for sparse linear systems. We also show how the architecture of the today's powerful RISC processors may influence efficient code design. 1 Introduction One of the common problems for application scientists is to exploit as efficiently as possible the hardware of high-performance computers (either serial or parallel) without totally rewriting or redesigning existing codes and algorithms. We believe that the availability of portable and efficient serial and parallel numerical libraries that ca...
