Development and application of the computational model for skeleton solutions. Case study - using “bag-of-task” for hrbf neural network learning

Abstract

The article proposes a solution to the problem of mapping an algorithm from the field of Computational Mathematics on the target computing environment. The solution is based on a formal method for constructing parallel skeletons. The method comprises a specification of concurrency with the directed graphs and a formula for interpretation of dynamic behavior of such graphs. This interpretation is based on Temporal Logic of Actions approach proposed by Leslie Lamport. To illustrate the use of the method the “bag-oftasks” parallel skeleton is discussed hereinafter. We present graphically basic skeleton operations with the proposed computational model. After that we specify a learning algorithm of hyper-radial basis function neural network in the terms of skeleton operations as a case study. This made it possible to parallelize the leaning algorithm and map it on desired computing environments with predefined run-time libraries. Computational experiments confirming that our approach does not reduce the performance of the resulting programs are presented. The approach is suitable for researchers not familiar with parallel computing. It helps to get a reliable and effective supercomputer application both for SMP and distributed architectures.

Keywords

• cluster
• supercomputing
• templet language
• pattern
• bag-of-tasks
• skeleton programming
• model of computation
• hrbf neural network