By Topic

A parallel programming model for irregular dynamic neural networks

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
$33 $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

1 Author(s)
L. Prechelt ; Fakultat fur Inf., Karlsruhe Univ., Germany

The compilation of high-level programming languages for parallel machines faces two challenges: maximizing data/process locality and balancing load. No solutions for the general case are known that solve both problems at once. The present paper describes a programming model that allows to solve both problems for the special case of neural network learning algorithms, even for irregular networks with dynamically changing topology (constructive neural algorithms). The model is based on the observation that such algorithms predominantly execute local operations (on nodes and connections of the network), reductions, and broadcasts. The model is concretized in an object-centered procedural language called CuPit. The language is completely abstract: No aspects of the parallel implementation such as number of processors, data distribution, process distribution, execution model etc. are visible in user programs. The compiler can derive most information relevant for the generation of efficient code from unannotated source code. Therefore, CuPit programs are efficiently portable. A compiler for CuPit has been built for the MasPar MP-1/MP-2 using compilation techniques that can also be applied to most other parallel machines. The paper shortly presents the main ideas of the techniques used and results obtained by the various optimizations

Published in:

Massively Parallel Programming Models, 1997. Proceedings. Third Working Conference on

Date of Conference:

12-14 Nov 1997