METHOD OF PARALLELIZATION ON BASIC MACRO OPERATIONS FOR PROCESSING LARGE SPARSE UNSTRUCTURED MATRIXES ON RCS

Abstract

Analysis calculating large sparse unstructured matrices (LSU-matrices) methods and tools
for cluster computing systems with a traditional architecture showed that for most tasks of processing
matrices with about 105 rows, performance compose reduced 5-7 times compared to the
peak performance. Meanwhile peak performance of computing systems is mainly estimated by the
LINPAC test, which involves the execution of matrix operations. The main goal of the work is to
increase the efficiency processing LSU-matrices, for this purpose advisable to use reconfigurable
computing systems (RSC) based on FPGAs as the main type of computing tools. For efficient processing
LSU-matrices on RCS, a set method and approaches previously described in the papers
are used, such as the structural organization of calculations, the format for representing
LSU-matrices "row of lines", the paradigm of discrete-event organization of data flows, the method of parallelization by iterations. The article considers the method of parallelization by basic
macro-operations for solving the problem of processing LSU-matrices on RCS, which implies
obtaining a constant computational efficiency, regardless of the portrait of processed LSUmatrices.
Using developed methods for processing LSU-matrices for reconfigurable computing
systems makes it possible to provide computational efficiency at the level of 50%, which is several
times superior to traditional parallelization methods