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dc.contributor.advisorCeze, Luis Hen_US
dc.contributor.authorNelson, Jacob Ericen_US
dc.date.accessioned2015-02-24T17:33:20Z
dc.date.available2015-02-24T17:33:20Z
dc.date.submitted2014en_US
dc.identifier.otherNelson_washington_0250E_13858.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/27449
dc.descriptionThesis (Ph.D.)--University of Washington, 2014en_US
dc.description.abstractGrappa is a modern take on software distributed shared memory (DSM) for in-memory data-intensive applications. Grappa enables users to program a cluster as if it were a single, large, non-uniform memory access (NUMA) machine. Performance scales up even for applications that have poor locality and input-dependent load distribution. Grappa addresses deficiencies of previous DSM systems by exploiting application parallelism, trading off latency for throughput. We evaluate Grappa with an in-memory map/reduce framework (10x faster than Spark); a vertex-centric framework inspired by GraphLab (1.33x faster than native GraphLab); and a relational query execution engine (12.5x faster than Shark). All these frameworks required only 60-690 lines of Grappa code.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjectgraph; irregular; latency; network; programming; throughputen_US
dc.subject.otherComputer scienceen_US
dc.subject.otherComputer engineeringen_US
dc.subject.othercomputer science and engineeringen_US
dc.titleLatency-Tolerant Distributed Shared Memory For Data-Intensive Applicationsen_US
dc.typeThesisen_US
dc.embargo.termsOpen Accessen_US


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