Title:
Data Prefetching Using Off-Line Learning

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dc.contributor.author Kim, Jinwoo en_US
dc.contributor.author Palem, Krishna V.
dc.contributor.author Wong, Weng Fai
dc.date.accessioned 2005-06-17T17:43:31Z
dc.date.available 2005-06-17T17:43:31Z
dc.date.issued 2001 en_US
dc.description.abstract An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from insertion of prefetch instructions by means of program analysis to strictly hardware prefetch mechanisms have been proposed. The former, however, is less successful for pointer intensive applications. In this paper, we propose a hardware solution that utilizes off-line learning algorithms. In essence, a sample trace of the application is fed into various off-line learning schemes. The results from these schemes are then loaded into a prefetching hardware at the appropriate point in the execution of the application to drive the prefetching. We propose a general architecture and scheme for such a process and report on the results of some of the experiments we performed. en_US
dc.format.extent 267237 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/6570
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries CC Technical Report; GIT-CC-01-17 en_US
dc.subject Prefetching
dc.subject Off-line learning
dc.subject Algorithms
dc.title Data Prefetching Using Off-Line Learning en_US
dc.type Text
dc.type.genre Technical Report
dspace.entity.type Publication
local.contributor.corporatename College of Computing
local.relation.ispartofseries College of Computing Technical Report Series
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication 35c9e8fc-dd67-4201-b1d5-016381ef65b8
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