NDLI: Shared work list: hacking amorphous data parallelism in UPC

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Shared work list: hacking amorphous data parallelism in UPC

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Shared work list: hacking amorphous data parallelism in UPC

Content Provider	ACM Digital Library
Author	Chen, Li Xu, Shixiong
Abstract	"Irregular" algorithms using data structures like sparse graphs, trees and sets prevail in the most emerging problems domains such as social network analysis, machine learning, data mining and computational science. The irregularity of underlying data structures leads to unstructured parallelism in these algorithms, consequently making it pretty hard for users to write efficient parallel implementations on distributed memory systems. Unified Parallel C language provides convenience of a global address space with the locality control needed for high performance and scalability. However, the Single Program Multiple Data execution model with a statically fixed set of executing threads makes UPC does not support applications with unstructured parallelism. In this paper, we first put forward Shared Work List to UPC and advocate a programming paradigm for writing applications with amorphous data parallelism on distributed memory systems. We also introduce user-assisted speculative execution based on Active Message model to support speculative execution on distributed memory systems. Efficient mechanism of work dispatching and related optimizations are presented as well. We preliminarily choose Breadth-first Search as a case study to demonstrate the feasibility, pro-grammability and performance benefits out of Shared Work List.
Starting Page	124
Ending Page	133
Page Count	10
File Format	PDF
ISBN	9781450312110
DOI	10.1145/2141702.2141716
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-02-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User-assisted speculative execution Unified parallel c Amorphous data parallelism Work list Irregular applications
Content Type	Text
Resource Type	Article

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