Exploiting mixed-mode parallelism for matrix operations on the HERA architecture through reconfiguration

X. Wang, S. G. Ziavras

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Recent advances in multi-million-gate platform field-programmable gate arrays (FPGAs) have made it possible to design and implement complex parallel systems on a programmable chip that also incorporate hardware floating-point units (FPUs). These options take advantage of resource reconfiguration. In contrast to the majority of the FPGA community that still employs reconfigurable logic to develop algorithm-specific circuitry, our FPGA-based mixed-mode reconfigurable computing machine can implement simultaneously a variety of parallel execution modes and is also user programmable. Our heterogeneous reconfigurable architecture (HERA) machine can implement the single-instruction, multiple-data (SIMD), multiple-instruction, multiple-data (MIMD) and multiple-SIMD (M-SIMD) execution modes. Each processing element (PE) is centred on a single-precision IEEE 754 FPU with tightly-coupled local memory, and supports dynamic switching between SIMD and MIMD at runtime. Mixed-mode parallelism has the potential to best match the characteristics of all subtasks in applications, thus resulting in sustained high performance. HERA's performance is evaluated by two common computation-intensive testbenches: matrix-matrix multiplication (MMM) and LU factorisation of sparse doubly-bordered-block-diagonal (DBBD) matrices. Experimental results with electrical power network matrices show that the mixed-mode scheduling for LU factorisation can result in speedups of about 19 and 15.5 compared to the SIMD and MIMD implementations, respectively.

Original languageEnglish (US)
Pages (from-to)249-260
Number of pages12
JournalIEE Proceedings: Computers and Digital Techniques
Volume153
Issue number4
DOIs
StatePublished - 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics

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