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dc.contributor.authorKambadur, Prabhanjan
dc.contributor.authorGhoting, Amol
dc.contributor.authorGupta, Anshul
dc.contributor.authorLumsdaine, Andrew
dc.date.accessioned2021-09-20T09:01:21Z
dc.date.available2021-09-20T09:01:21Z
dc.date.issued2010
dc.identifier.citationKambadur Prabhanjan, Ghoting Amol, Gupta Anshul, & Lumsdaine Andrew. (2010). Extending Task Parallelism For Frequent Pattern Mining [JB]. Advances in Parallel Computing, 19(Parallel Computing: From Multicores and GPU’s to Petascale), 289–296. https://doi.org/10.3233/978-1-60750-530-3-289
dc.identifier.isbn9781607505297
dc.identifier.issn0927-5452
dc.identifier.doi10.3233/978-1-60750-530-3-289
dc.identifier.urihttp://hdl.handle.net/10754/671343
dc.description.abstractAlgorithms for frequent pattern mining, a popular informatics application, have unique requirements that are not met by any of the existing parallel tools. In particular, such applications operateon extremelylarge data sets and have irregular memory access patterns. For efficient parallelization of such applications, it is necessary to support dynamic load balancing along with scheduling mechanisms that allow users to exploit data locality. Given these requirements, task parallelism is the most promising of the available parallel programming models. However, existing solutions for task parallelism schedule tasks implicitly and hence, custom scheduling policies that can exploit data locality cannot be easily employed. In this paper we demonstrate and characterize the speedup obtained in a frequent pattern mining application using a custom clustered scheduling policy in place of the popular Cilk-style policy. We present PFunc, a novel task parallel library whose customizable task scheduling and task priorities facilitated the implementation of our clustered scheduling policy. © 2010 The authors and IOS Press. All rights reserved.
dc.description.sponsorshipWe thank Melanie Dybvig for her help in improving the quality of this paper. Our work was supported by IBM, King Abdullah University of Science and Technology (KAUST), National Science Foundation grants EIA-0202048 and CCF-0541335, and a grant from the Lilly Endowment.
dc.publisherIOS Press BV
dc.relation.urlhttps://www.medra.org/servlet/aliasResolver?alias=iospressISSNISBN&issn=0927-5452&volume=19&spage=289
dc.rightsArchived with thanks to IOS Press BV
dc.subjecttask parallelism
dc.subjectfrequent pattern mining
dc.subjectdata locality
dc.titleExtending Task Parallelism For Frequent Pattern Mining
dc.typeConference Paper
dc.identifier.wosutWOS:000393292700033
dc.eprint.versionPre-print
dc.contributor.institutionOpen Systems Lab, Indiana University, Bloomington, IN-47408, United States
dc.contributor.institutionIBM T J Watson Research Center, Yorktown Heights, NY-10598, United States
dc.identifier.volume19
dc.identifier.pages289-296
dc.identifier.eid2-s2.0-84906504339
kaust.acknowledged.supportUnitCCF


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