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A model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality

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dc.contributor.authorDinkins, Stephanie, author
dc.contributor.authorStrout, Michelle Mills, advisor
dc.contributor.authorRajopadhye, Sanjay V., committee member
dc.contributor.authorMueller, Jennifer L., committee member
dc.date.accessioned2007-01-03T08:03:02Z
dc.date.available2007-01-03T08:03:02Z
dc.date.issued2012
dc.description.abstractSparse matrix vector multiply (SpMV) is an important computation that is used in many scientific and structural engineering applications. Sparse computations like SpMV require the use of special sparse data structures that efficiently store and access non-zeros. However, sparse data structures can tax the memory bandwidth of a machine and limit the performance of a computation, which for these computations is typically less than 10% of a processor's peak floating point performance. The goal of this thesis was to understand the trade-off between memory bandwidth needs and data locality for SpMV performance. We construct three performance models based on memory bandwidth requirements and the data locality that is associated with the non-zero orderings within a sparse data structure. Our approach uses metrics that can be applied to any sparse data structure to model SpMV performance. We use a data locality metric termed Manhattan distance to predict the data locality execution time of each non-zero ordering, which produced strong per matrix results. Based on the per matrix results for the Manhattan distance we constructed a model that computes a linear fit for multiple parameters, but those results were not promising. We found that the memory bandwidth requirement for a data structure is a key component to predicting performance. Our final model uses memory bandwidth pressure and an averaged predicted data locality time to accurately predict the fastest performing data structure 73-84% of the time, depending upon the machine. Our results indicate that often the sparse data structure with the least taxation on the memory bandwidth produces the fastest execution times.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDinkins_colostate_0053N_10913.pdf
dc.identifierETDF2012500029COMS
dc.identifier.urihttp://hdl.handle.net/10217/65303
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectdata locality
dc.subjectManhattan distance
dc.subjectperformance model
dc.subjectsparse matrices
dc.subjectsparse matrix vector multiply
dc.subjectSpMV
dc.titleA model for predicting the performance of sparse matrix vector multiply (SpMV) using memory bandwidth requirements and data locality
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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