A parallel programming model for irregular dynamic neural networks

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.