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CSX: An extended compression format for SpMV on shared memory systems

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dc.contributor.author Kourtis, K en
dc.contributor.author Karakasis, V en
dc.contributor.author Goumas, G en
dc.contributor.author Koziris, N en
dc.date.accessioned 2014-03-01T02:47:19Z
dc.date.available 2014-03-01T02:47:19Z
dc.date.issued 2011 en
dc.identifier.issn 0362-1340 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/33074
dc.subject Compression en
dc.subject Shared memory en
dc.subject SMP en
dc.subject Sparse matrix-vector multiplication en
dc.subject SpMV en
dc.subject.classification Computer Science, Software Engineering en
dc.subject.other Data volume en
dc.subject.other ITS data en
dc.subject.other matrix en
dc.subject.other Multiple processing en
dc.subject.other Offline en
dc.subject.other Performance Gain en
dc.subject.other Run-time code generation en
dc.subject.other Shared memories en
dc.subject.other Shared memory system en
dc.subject.other SMP en
dc.subject.other Sparse matrices en
dc.subject.other Sparse matrix-vector multiplication en
dc.subject.other SpMV en
dc.subject.other Storage formats en
dc.subject.other Metadata en
dc.subject.other Matrix algebra en
dc.title CSX: An extended compression format for SpMV on shared memory systems en
heal.type conferenceItem en
heal.identifier.primary 10.1145/2038037.1941587 en
heal.identifier.secondary http://dx.doi.org/10.1145/2038037.1941587 en
heal.language English en
heal.publicationDate 2011 en
heal.abstract The Sparse Matrix-Vector multiplication (SpMV) kernel scales poorly on shared memory systems with multiple processing units due to the streaming nature of its data access pattern. Previous re- search has demonstrated that an effective strategy to improve the kernel's performance is to drastically reduce the data volume in- volved in the computations. Since the storage formats for sparse matrices include metadata describing the structure of non-zero el- ements within the matrix, we propose a generalized approach to compress metadata by exploiting substructures within the matrix. We call the proposed storage format Compressed Sparse eXtended (CSX). In our implementation we employ runtime code generation to construct specialized SpMV routines for each matrix. Experi- mental evaluation on two shared memory systems for 15 sparse matrices demonstrates significant performance gains as the number of participating cores increases. Regarding the cost of CSX con- struction, we propose several strategies which trade performance for preprocessing cost making CSX applicable both to online and offline preprocessing. Copyright © 2011 ACM. en
heal.publisher ASSOC COMPUTING MACHINERY en
heal.journalName ACM SIGPLAN Notices en
dc.identifier.doi 10.1145/2038037.1941587 en
dc.identifier.isi ISI:000296264900025 en
dc.identifier.volume 46 en
dc.identifier.issue 8 en
dc.identifier.spage 247 en
dc.identifier.epage 256 en


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