Parallelization of Stochastic Evolution for Cell Placement

VLSI physical design and the problems related to it such as placement, channel routing, etc, carry inherent complexities that are best dealt with iterative heuristics. However the major drawback of these iterative heuristics has been the large runtime involved in reaching acceptable solutions especi...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي: Khan, Khawar S. (author)
مؤلفون آخرون: unknown (author)
التنسيق: other
منشور في: 2004
الموضوعات:
الوصول للمادة أونلاين:https://eprints.kfupm.edu.sa/id/eprint/278/1/proposal.pdf
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author Khan, Khawar S.
author2 unknown
author2_role author
author_facet Khan, Khawar S.
unknown
author_role author
dc.creator.none.fl_str_mv Khan, Khawar S.
unknown
dc.date.none.fl_str_mv 2004
2020
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/278/1/proposal.pdf
(2004) Parallelization of Stochastic Evolution for Cell Placement. KFUPM. (Unpublished)
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv KFUPM
dc.relation.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/278/
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Computer
dc.title.none.fl_str_mv Parallelization of Stochastic Evolution for Cell Placement
dc.type.none.fl_str_mv Other
NonPeerReviewed
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/other
description VLSI physical design and the problems related to it such as placement, channel routing, etc, carry inherent complexities that are best dealt with iterative heuristics. However the major drawback of these iterative heuristics has been the large runtime involved in reaching acceptable solutions especially when optimizing for multiple objectives. Among the acceleration techniques proposed, parallelization is one promising method. Distributed memory multiprocessor systems and shared memory multiprocessor systems have gained considerable attention in recent years of research. This idea of parallel computing has attracted both the researchers and manufacturers who are targeting to reduce the time to market. Our objective is to exploit the benefits of parallel computing for a time consuming placement problem in VLSI. Finding the best solution for the placement of n modules is a hard problem. Thus the enumerative search techniques, specially those which employ the brute force, are unaccepted for the circuits in which n (number of modules) is large. Constructive and Iterative heuristics play the key role in this scenario and hence are frequently used. We will use Stochastic Evolution for finding the optimal solution to the above mentioned placement problem where the major task in our objective will be the parallelization of Stochastic Evolution using different parallelization techniques and the comparison between these different parallelized versions based on the results achieved. The parallelization will be carried out using MPI (Message Passing Interface) on a distributed memory multiprocessor system and conclusion will be based on the results achieved that are expected to show speedup nearly equal to linear speedup when run over increasing number of processors.
eu_rights_str_mv openAccess
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language_invalid_str_mv en
network_acronym_str KFUPM
network_name_str King Fahd University of Petroleum and Minerals
oai_identifier_str oai::278
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spelling Parallelization of Stochastic Evolution for Cell PlacementKhan, Khawar S.unknownComputerVLSI physical design and the problems related to it such as placement, channel routing, etc, carry inherent complexities that are best dealt with iterative heuristics. However the major drawback of these iterative heuristics has been the large runtime involved in reaching acceptable solutions especially when optimizing for multiple objectives. Among the acceleration techniques proposed, parallelization is one promising method. Distributed memory multiprocessor systems and shared memory multiprocessor systems have gained considerable attention in recent years of research. This idea of parallel computing has attracted both the researchers and manufacturers who are targeting to reduce the time to market. Our objective is to exploit the benefits of parallel computing for a time consuming placement problem in VLSI. Finding the best solution for the placement of n modules is a hard problem. Thus the enumerative search techniques, specially those which employ the brute force, are unaccepted for the circuits in which n (number of modules) is large. Constructive and Iterative heuristics play the key role in this scenario and hence are frequently used. We will use Stochastic Evolution for finding the optimal solution to the above mentioned placement problem where the major task in our objective will be the parallelization of Stochastic Evolution using different parallelization techniques and the comparison between these different parallelized versions based on the results achieved. The parallelization will be carried out using MPI (Message Passing Interface) on a distributed memory multiprocessor system and conclusion will be based on the results achieved that are expected to show speedup nearly equal to linear speedup when run over increasing number of processors.KFUPM20042020OtherNonPeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/otherapplication/pdfhttps://eprints.kfupm.edu.sa/id/eprint/278/1/proposal.pdf (2004) Parallelization of Stochastic Evolution for Cell Placement. KFUPM. (Unpublished) enhttps://eprints.kfupm.edu.sa/id/eprint/278/info:eu-repo/semantics/openAccessoai::2782019-11-01T13:23:25Z
spellingShingle Parallelization of Stochastic Evolution for Cell Placement
Khan, Khawar S.
Computer
status_str publishedVersion
title Parallelization of Stochastic Evolution for Cell Placement
title_full Parallelization of Stochastic Evolution for Cell Placement
title_fullStr Parallelization of Stochastic Evolution for Cell Placement
title_full_unstemmed Parallelization of Stochastic Evolution for Cell Placement
title_short Parallelization of Stochastic Evolution for Cell Placement
title_sort Parallelization of Stochastic Evolution for Cell Placement
topic Computer
url https://eprints.kfupm.edu.sa/id/eprint/278/1/proposal.pdf