DISCRETE-EVENT METHOD COMPUTATIONS ORGANIZING FOR PROCESSING LARGE SPARSE UNSTRUCTURED MATRIXES ON RCS
Abstract
Increasing models complexity objects and processes study, in different sphere of science and
technology, set up plenty issues to necessary to use high-performance computing systems. Arrays
matrix processing by cluster multiprocessor computing systems in conjunction special methods
aimed at organizing parallel computations, basically obtain computing performance system is
quite high. However, that computational efficiency is not observed for all types of matrices. Matrix
structure be in a position contain large amount of insignificant elements, large dimension and
unstructured portrait. Calculation execute for described kind of matrices on cluster multiprocessorcomputing system couldn't achieve close peak performance. Considering that processing methods
leave out the complex structure of the matrix being processed. As a result, the performance of the
system is significantly reduced. The development of cluster MCS methods doesn't allow for full
ensure 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 such
matrices, and lead to non-uniformity loading processor. To achieve performance close the peak
for tasks large sparse unstructured matrices processing necessary to use reconfigurable computing
systems. RCS architecture allows adapting computation structure to the problem solved. This
makes it possible to organize pipeline processing, such a way that computational resource RCS
used only for informational significant operations. In addition using generally accepted methods
for structural organization of high-performance computing for RCS, it is necessary to develop a
format for storing and transferring large sparse unstructured matrices, to determine the principles
of constructing basic matrix macro-operations and the possibility of organizing composite discrete-
event matrix functions for solving applied problems. Сconsequently method founding laid
allows organizing computations operands, which are large sparse unstructured matrices. The
application this method for organizing computations can significantly increase productivity, and
provide an increase in the efficiency of such a system.
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