How to improve the performance of programs calling mathematical functions

Taking advantage of IBM XL C/C++ or XL Fortran compiler auto-vectorization

From the developerWorks archives

Robert Enenkel and Daniel Zabawa

Date archived: December 20, 2016 | First published: April 13, 2010

This article introduces the IBM MASS high-performance mathematical libraries, and demonstrates how to benefit from them — without the need for source program changes — by using the auto-vectorization capability of the IBM® XL C/C++ and XL Fortran compilers. After introducing the concept of auto-vectorization and the associated compiler options, a case study of a discrete Fourier transform program is offered as a real life example of auto-vectorization. Timing results demonstrate that speedups of up to 8.94 times are obtained by the compilers on the example program, via the automatic invocation of MASS by auto-vectorization.

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Zone=Rational, Multicore acceleration
ArticleTitle=How to improve the performance of programs calling mathematical functions