The Problem with the Linpack Benchmark Matrix Generator

We characterize the matrix sizes for which the Linpack Benchmark matrix generator constructs a matrix with identical columns

Squeezing the most out of eigenvalue solvers on high-performance computers

AbstractThis paper describes modifications to many of the standard algorithms used in computing eigenvalues and eigenvectors of matrices. These modifications can dramatically increase the performance of the underlying software on high-performance computers without resorting to assembler language, without significantly influencing the floating-point operation count, and without affecting the roundoff-error properties of the algorithms. The techniques are applied to a wide variety of algorithms and are beneficial in various architectural settings

Developing numerical libraries in Java

The rapid and widespread adoption of Java has created a demand for reliable and reusable mathematical software components to support the growing number of compute-intensive applications now under development, particularly in science and engineering. In this paper we address practical issues of the Java language and environment which have an effect on numerical library design and development. Benchmarks which illustrate the current levels of performance of key numerical kernels on a variety of Java platforms are presented. Finally, a strategy for the development of a fundamental numerical toolkit for Java is proposed and its current status is described.Comment: 11 pages. Revised version of paper presented to the 1998 ACM Conference on Java for High Performance Network Computing. To appear in Concurrency: Practice and Experienc