Parallel LU factorization of sparse matrices on FPGA-based configurable computing engines

Xiaofang Wang, Sotirios G. Ziavras

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Configurable computing, where hardware resources are configured appropriately to match specific hardware designs, has recently demonstrated its ability to significantly improve performance for a wide range of computation-intensive applications. With steady advances in silicon technology, as predicted by Moore's Law, Field-Programmable Gate Array (FPGA) technologies have enabled the implementation of System-on-a-Programmable-Chip (SOPC or SOC) computing platforms, which, in turn, have given a significant boost to the field of configurable computing. It is possible to implement various specialized parallel machines in a single silicon chip. In this paper, we describe our design and implementation of a parallel machine on an SOPC development board, using multiple instances of a soft IP configurable processor; we use this machine for LU factorization. LU factorization is widely used in engineering and science to solve efficiently large systems of linear equations. Our implementation facilitates the efficient solution of linear equations at a cost much lower than that of supercomputers and networks of workstations. The intricacies of our FPGA-based design are presented along with tradeoff choices made for the purpose of illustration. Performance results prove the viability of our approach.

Original languageEnglish (US)
Pages (from-to)319-343
Number of pages25
JournalConcurrency Computation Practice and Experience
Issue number4
StatePublished - Apr 10 2004

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computer Science Applications
  • Computational Theory and Mathematics


  • FPGA
  • Hardware design
  • LU factorization
  • Matrix inversion
  • Parallel processing


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