An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable Computing

As the number of cores per discrete integrated circuit (IC) device grows, the importance of the network on chip (NoC) increases. However, the body of research in this area has focused on discrete IC devices alone which may or may not serve the high-performance computing community which needs to ass...

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Main Authors: Andrew G. Schmidt, William V. Kritikos, Shanyuan Gao, Ron Sass
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:International Journal of Reconfigurable Computing
Online Access:http://dx.doi.org/10.1155/2012/564704
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author Andrew G. Schmidt
William V. Kritikos
Shanyuan Gao
Ron Sass
author_facet Andrew G. Schmidt
William V. Kritikos
Shanyuan Gao
Ron Sass
author_sort Andrew G. Schmidt
collection DOAJ
description As the number of cores per discrete integrated circuit (IC) device grows, the importance of the network on chip (NoC) increases. However, the body of research in this area has focused on discrete IC devices alone which may or may not serve the high-performance computing community which needs to assemble many of these devices into very large scale, parallel computing machines. This paper describes an integrated on-chip/off-chip network that has been implemented on an all-FPGA computing cluster. The system supports MPI-style point-to-point messages, collectives, and other novel communication. Results include the resource utilization and performance (in latency and bandwidth).
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spelling doaj-art-a0a401da9da0404f90c699e132e82df12025-02-03T05:47:35ZengWileyInternational Journal of Reconfigurable Computing1687-71951687-72092012-01-01201210.1155/2012/564704564704An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable ComputingAndrew G. Schmidt0William V. Kritikos1Shanyuan Gao2Ron Sass3Information Sciences Institute, University of Southern California, 3811 North Fairfax Drive, Suite 200, Arlington, VA 22203, USAReconfigurable Computing Systems Lab, UNC Charlotte, Electrical and Computer Engineering Department, 9201 University City Boulevard, Charlotte, NC 28223, USAReconfigurable Computing Systems Lab, UNC Charlotte, Electrical and Computer Engineering Department, 9201 University City Boulevard, Charlotte, NC 28223, USAReconfigurable Computing Systems Lab, UNC Charlotte, Electrical and Computer Engineering Department, 9201 University City Boulevard, Charlotte, NC 28223, USAAs the number of cores per discrete integrated circuit (IC) device grows, the importance of the network on chip (NoC) increases. However, the body of research in this area has focused on discrete IC devices alone which may or may not serve the high-performance computing community which needs to assemble many of these devices into very large scale, parallel computing machines. This paper describes an integrated on-chip/off-chip network that has been implemented on an all-FPGA computing cluster. The system supports MPI-style point-to-point messages, collectives, and other novel communication. Results include the resource utilization and performance (in latency and bandwidth).http://dx.doi.org/10.1155/2012/564704
spellingShingle Andrew G. Schmidt
William V. Kritikos
Shanyuan Gao
Ron Sass
An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable Computing
International Journal of Reconfigurable Computing
title An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable Computing
title_full An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable Computing
title_fullStr An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable Computing
title_full_unstemmed An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable Computing
title_short An Evaluation of an Integrated On-Chip/Off-Chip Network for High-Performance Reconfigurable Computing
title_sort evaluation of an integrated on chip off chip network for high performance reconfigurable computing
url http://dx.doi.org/10.1155/2012/564704
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