By Topic

Deriving optimal data distributions for group parallel numerical algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Rauber, T. ; Dept. of Comput. Sci., Saarlandes Univ., Saarbrucken, Germany ; Runger, G. ; Wilhelm, R.

Numerical algorithms often exhibit potential parallelism caused by a coarse structure of submethods in addition to the medium grain parallelism of systems within submethods. We present a derivation methodology for parallel programs of numerical methods on distributed memory machines that exploits both levels of parallelism in a group-SPMD parallel computation model. The derivation process starts with a specification of the numerical method in a module structure of submethods, and results in a parallel frame program containing all implementation decisions of the parallel implementation. The implementation derivation includes scheduling of modules, assigning processors to modules and choosing data distributions for basic modules. The methodology eases parallel programming and supplies a formal basis for automatic support. An analysis model allows performance predictions for parallel frame programs. In this article we concentrate on the determination of optimal data distributions using a dynamic programming approach based on data distribution types and incomplete run-time formulas

Published in:

Programming Models for Massively Parallel Computers, 1995

Date of Conference:

9-12 Oct 1995