Adaptive scheduling of array-intensive applications on mixed-mode reconfigurable multiprocessors

Xiaofang Wang, Sotirios G. Ziavras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Array-based applications are normally computation-intensive and many customized designs have been proposed targeting FPGA (Field-Programmable Gate Array) implementations to accelerate their real-time solutions. Most often a time-consuming procedure to convert floating-point representations for a fixed-point implementation is necessary due to insufficient hardware resources. Recent remarkable advances in state-of-the-art FPGAs provide new opportunities. This paper presents a dynamic scheduling strategy that applies novel application characterization and adaptive allocation of processors among the active tasks; we show results for our in-house developed mixed-mode MPoPC (MultipProcessor-on- a-Programmable-Chip) HERA (Heterogeneous Reconfigurable Architecture) machine. The innovative adaptive parallelization technique combined with the unique flexibilities provided by the reconfigurable logic help to achieve high utilization of resources and potentially minimum execution times. Experimental results for parallel Singular Value Decomposition (SVD) on Xilinx Virtex II FPGAs are reported.

Original languageEnglish (US)
Title of host publicationConference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers
Pages1642-1646
Number of pages5
StatePublished - 2005
Event39th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Oct 28 2005Nov 1 2005

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2005
ISSN (Print)1058-6393

Other

Other39th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove, CA
Period10/28/0511/1/05

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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