Harnessing parallelism in multicore clusters with the all-pairs and wavefront abstractions

Authors:
Li Yi;Christopher Moretti;Scott Emrich;Kenneth Judd;Douglas Thain
Affiliations:
University of Notre Dame, Notre Dame, IN, USA;University of Notre Dame, Notre Dame, IN, USA;University of Notre Dame, Notre Dame, IN, USA;Stanford University, Stanford, CA, USA;University of Notre Dame, Notre Dame, IN, USA
Venue:
Proceedings of the 18th ACM international symposium on High performance distributed computing
Year:
2009

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Abstract

Both distributed systems and multicore computers are difficult programming environments. Although the expert programmer may be able to tune distributed and multicore computers to achieve high performance, the non-expert may struggle to achieve a program that even functions correctly. We argue that high level abstractions are an effective way of making parallel computing accessible to the non-expert. An abstraction is a regularly structured framework into which a user may plug in simple sequential programs to create very large parallel programs. By virtue of a regular structure and declarative specification, abstractions may be materialized on distributed, multicore, and distributed multicore systems with robust performance across a wide range of problem sizes. In previous work, we presented the All-Pairs abstraction for computing on distributed systems of single CPUs. In this paper, we extend All-Pairs to multicore systems, and introduce Wavefront, which represents a number of problems in economics and bioinformatics. We demonstrate good scaling of both abstractions up to 32-cores on one machine and hundreds of cores in a distributed system.