Modified method large sparse unstructured matrices processing on reconfigurable computing systems


  • Ilya I. Levin
  • Aleksandr V. Podoprigora


reconfigurable computing systems
high-performance computing systems
sparse matrix
large unstructured matrix
sparse matrix format
discrete-event transformation
intensity balance dataflow
parallelization of calculations
parallelization over non-zero elements


When processing high-dimensional matrices with an irregular structure, the realperformance of cluster multiprocessor computing systems (MCS) is low and even with the use of special processing methods does not exceed 30%. To effectively process large matrices with an irregular structure, it is possible to use reconfigurable computing systems (RCS), for which the authors proposed a method for processing large sparse unstructured (LSU) matrices, due to which real performance was achieved close to 50% of the peak. The article describes a modification of the developed method for processing LSU matrices, which is characterized by parallel processing of non-zero row elements and allows doubling the speed of the computing structure with a slight increase in the occupied hardware resource. The modified method of processing LSU matrices on an RCS provides real performance close to 90% of the peak, which significantly exceeds the known results of solving similar problems for cluster MCS. Comparison of the results of solving the problem of ranking web pages using the PageRank algorithm obtained on the “Arcturus” RCS and the Fugaku supercomputer, as well as the results of solving the SLAE using the Jacobi method on the “Arcturus” RCS and the graphics accelerator NVidia Tesla K40 confirms the theoretical conclusions.





Parallel software tools and technologies

Author Biographies

Ilya I. Levin

South Federal University,
Institute of Computer Technology and Information Security,
Department of Intelligent and Multiprocessor Systems
• Head of Department

Aleksandr V. Podoprigora


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