МЕТОД ОРГАНИЗАЦИИ ДИСКРЕТНО-СОБЫТИЙНЫХ ВЫЧИСЛЕНИЙ ДЛЯ ОБРАБОТКИ БОЛЬШИХ РАЗРЕЖЕННЫХ НЕСТРУКТУРИРОВАННЫХ МАТРИЦ НА РВС

Abstract

Increasing models complexity objects and processes study, in different sphere of science andtechnology, set up plenty issues to necessary to use high-performance computing systems. Arraysmatrix processing by cluster multiprocessor computing systems in conjunction special methodsaimed at organizing parallel computations, basically obtain computing performance system isquite high. However, that computational efficiency is not observed for all types of matrices. Matrixstructure be in a position contain large amount of insignificant elements, large dimension andunstructured portrait. Calculation execute for described kind of matrices on cluster multiprocessorcomputing system couldn't achieve close peak performance. Considering that processing methodsleave out the complex structure of the matrix being processed. As a result, the performance of thesystem is significantly reduced. The development of cluster MCS methods doesn't allow for fullensure high performance for class of problems processing of large sparse unstructured matrices.Rigid architecture of processor commutation net doesn’t take into account the peculiarities of suchmatrices, and lead to non-uniformity loading processor. To achieve performance close the peakfor tasks large sparse unstructured matrices processing necessary to use reconfigurable computingsystems. RCS architecture allows adapting computation structure to the problem solved. Thismakes it possible to organize pipeline processing, such a way that computational resource RCSused only for informational significant operations. In addition using generally accepted methodsfor structural organization of high-performance computing for RCS, it is necessary to develop aformat for storing and transferring large sparse unstructured matrices, to determine the principlesof constructing basic matrix macro-operations and the possibility of organizing composite discrete-event matrix functions for solving applied problems. Сconsequently method founding laidallows organizing computations operands, which are large sparse unstructured matrices. Theapplication this method for organizing computations can significantly increase productivity, andprovide an increase in the efficiency of such a system.