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Acceleration of PageRank with Customized Precision Based on Mantissa Segmentation
dc.contributor.author | Grützmacher, Thomas | |
dc.contributor.author | Cojean, Terry | |
dc.contributor.author | Flegar, Goran | |
dc.contributor.author | Anzt, Hartwig | |
dc.contributor.author | Quintana-Orti, Enrique S. | |
dc.date.accessioned | 2020-07-28T07:48:02Z | |
dc.date.available | 2020-07-28T07:48:02Z | |
dc.date.issued | 2020-03 | |
dc.identifier.citation | Thomas Grützmacher, Terry Cojean, Goran Flegar, Hartwig Anzt, and Enrique S. Quintana-Ortí. 2020. Acceleration of PageRank with Customized Precision Based on Mantissa Segmentation. ACM Trans. Parallel Comput. 7, 1, Article 4 (March 2020), 19 pages. https://doi.org/10.1145/3380934 | |
dc.identifier.issn | 2329-4949 | |
dc.identifier.issn | 2329-4957 | |
dc.identifier.uri | http://hdl.handle.net/10234/189296 | |
dc.description.abstract | We describe the application of a communication-reduction technique for the PageRank algorithm that dynamically adapts the precision of the data access to the numerical requirements of the algorithm as the iteration converges. Our variable-precision strategy, using a customized precision format based on mantissa segmentation (CPMS), abandons the IEEE 754 single- and double-precision number representation formats employed in the standard implementation of PageRank, and instead handles the data in memory using a customized floating-point format. The customized format enables fast data access in different accuracy, prevents overflow/ underflow by preserving the ieee 754 double-precision exponent, and efficiently avoids data duplication, since all bits of the original ieee 754 double-precision mantissa are preserved in memory, but re-organized for efficient reduced precision access. With this approach, the truncated values (omitting significand bits), as well as the original ieee double-precision values, can be retrieved without duplicating the data in different formats. Our numerical experiments on an NVIDIA V100 GPU (Volta architecture) and a server equipped with two Intel Xeon Platinum 8168 CPUs (48 cores in total) expose that, compared with a standard ieee double-precision implementation, the CPMS-based PageRank completes about 10% faster if high-accuracy output is needed, and about 30% faster if reduced output accuracy is acceptable. | ca_CA |
dc.format.extent | 19 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Association for Computing Machinery (ACM) | ca_CA |
dc.relation.isPartOf | ACM Transactions on Parallel Computing, 2020, vol. 7, no 1 | ca_CA |
dc.rights | Copyright © Association for Computing Machinery | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | PageRank | ca_CA |
dc.subject | large-scale irregular graphs | ca_CA |
dc.subject | adaptive-precision | ca_CA |
dc.subject | high-performance | ca_CA |
dc.subject | multi-core processors | ca_CA |
dc.subject | GPUs | ca_CA |
dc.title | Acceleration of PageRank with Customized Precision Based on Mantissa Segmentation | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1145/3380934 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | https://dl.acm.org/doi/fullHtml/10.1145/3380934 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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