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Latency-Tolerant Software Distributed Shared Memory

Authors: 

Jacob Nelson, Brandon Holt, Brandon Myers, Preston Briggs, Luis Ceze, Simon Kahan, and Mark Oskin, University of Washington
Awarded Best Paper!

Abstract: 

We present Grappa, 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 MapReduce 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.

Jacob Nelson, University of Washington

Brandon Holt, University of Washington

Brandon Myers, University of Washington

Preston Briggs, University of Washington

Luis Ceze, University of Washington

Simon Kahan, University of Washington

Mark Oskin, University of Washington

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BibTeX
@inproceedings {190521,
author = {Jacob Nelson and Brandon Holt and Brandon Myers and Preston Briggs and Luis Ceze and Simon Kahan and Mark Oskin},
title = {{Latency-Tolerant} Software Distributed Shared Memory},
booktitle = {2015 USENIX Annual Technical Conference (USENIX ATC 15)},
year = {2015},
isbn = {978-1-931971-225},
address = {Santa Clara, CA},
pages = {291--305},
url = {https://www.usenix.org/conference/atc15/technical-session/presentation/nelson},
publisher = {USENIX Association},
month = jul,
}
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