Sparse Matrix Computations on Reconfigurable Hardware.
The article discusses how using a high-level language to hardware-description-language compiler with the Jacobi and conjugate gradient methods can be used to aid reconfigurable supercomputing and produce at least a twofold speedup. The authors tested both the Jacobi and conjugate gradient (CG) metho...
| Published in: | Computer (00189162) Vol. 40; no. 3; pp. 58 - 65 |
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| Main Authors: | , |
| Format: | Article |
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IEEE
Mar2007
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=24466917&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 24466917 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: Mar2007 vid: 40 iid: 3 pid: 13605 pub: IEEE artinfo: ui: 24466917 10.1109/MC.2007.103 ppf: 58 ppct: 7 formats: tig: atl: Sparse Matrix Computations on Reconfigurable Hardware. aug: au: Prasanna, Viktor K. Morris, Gerald R. affil: University of Southern California US Army Engineer Research and Development Center su: Field programmable gate arrays Conjugate gradient methods Compilers (Computer programs) Jacobi method Algorithms High performance processors Programming languages Adaptive computing systems Supercomputers sug: subj: Field programmable gate arrays Conjugate gradient methods Compilers (Computer programs) Jacobi method Algorithms High performance processors Programming languages Adaptive computing systems Supercomputers ab: The article discusses how using a high-level language to hardware-description-language compiler with the Jacobi and conjugate gradient methods can be used to aid reconfigurable supercomputing and produce at least a twofold speedup. The authors tested both the Jacobi and conjugate gradient (CG) methods in computers that combine general purpose processors with field-programmable gate arrays (FPGA) and those that run software only versions. They found that except for one instance, the FPGA-augmented version of CG and Jacobi outperformed the software. They believe because they used the same compiler, code and algorithm, run on the same platform, and use the same input sets, the comparisons show that the acceleration in processing speed is due to the FPGA. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2007 holdings: @attributes: islocal: N |
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