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dc.contributor.authorWaterland, Amos
dc.contributor.authorAngelino, Elaine
dc.contributor.authorAdams, Ryan Prescott
dc.contributor.authorAppavoo, Jonathan
dc.contributor.authorSeltzer, Margo I.
dc.date.accessioned2017-10-30T17:53:39Z
dc.date.issued2014
dc.identifier.citationWaterland, Amos, Elaine Angelino, Ryan P. Adams, Jonathan Appavoo, and Margo Seltzer. 2014. "ASC: automatically scalable computation." In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, March 1-5, 2014, Salt Lake City, UT: 575-590.en_US
dc.identifier.isbn978-1-4503-2305-5en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34309064
dc.description.abstractWe present an architecture designed to transparently and automatically scale the performance of sequential programs as a function of the hardware resources available. The architecture is predicated on a model of computation that views program execution as a walk through the enormous state space composed of the memory and registers of a single-threaded processor. Each instruction execution in this model moves the system from its current point in state space to a deterministic subsequent point. We can parallelize such execution by predictively partitioning the complete path and speculatively executing each partition in parallel. Accurately partitioning the path is a challenging prediction problem. We have implemented our system using a functional simulator that emulates the x86 instruction set, including a collection of state predictors and a mechanism for speculatively executing threads that explore potential states along the execution path. While the overhead of our simulation makes it impractical to measure speedup relative to native x86 execution, experiments on three benchmarks show scalability of up to a factor of 256 on a 1024 core machine when executing unmodified sequential programs.en_US
dc.description.sponsorshipEngineering and Applied Sciencesen_US
dc.language.isoen_USen_US
dc.publisherAssociation of Computing Machineryen_US
dc.relation.isversionofdoi:10.1145/2541940.2541985en_US
dc.relation.hasversionhttp://www.eecs.harvard.edu/~elaine/pubs/asplos14.pdfen_US
dash.licenseOAP
dc.subjectLearning—Connectionism and neural netsen_US
dc.subjectSuper (very large) computersen_US
dc.titleASC: Automatically Scalable Computationen_US
dc.typeConference Paperen_US
dc.description.versionAccepted Manuscripten_US
dc.relation.journalProceedings of the 19th international conference on Architectural support for programming languages and operating systems - ASPLOS '14en_US
dash.depositing.authorSeltzer, Margo I.
dc.date.available2017-10-30T17:53:39Z
dc.identifier.doi10.1145/2541940.2541985*
workflow.legacycommentsFAR 2014en_US
dash.contributor.affiliatedSeltzer, Margo
dash.contributor.affiliatedAdams, Ryan Prescott


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