Harnessing parallelism in multicore clusters with the All-Pairs, Wavefront, and Makeflow abstractions

  • Authors:

  • Li Yu;Christopher Moretti;Andrew Thrasher;Scott Emrich;Kenneth Judd;Douglas Thain

  • Affiliations:

  • Department of Computer Science and Engineering, University of Notre Dame, South Bend, USA;Department of Computer Science and Engineering, University of Notre Dame, South Bend, USA;Department of Computer Science and Engineering, University of Notre Dame, South Bend, USA;Department of Computer Science and Engineering, University of Notre Dame, South Bend, USA;Hoover Institution, Stanford University, Stanford, USA;Department of Computer Science and Engineering, University of Notre Dame, South Bend, USA

  • Venue:
  • Cluster Computing
  • Year:
  • 2010

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Abstract

Both distributed systems and multicore systems are difficult programming environments. Although the expert programmer may be able to carefully tune these systems to achieve high performance, the non-expert may struggle. 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 the Wavefront and Makeflow abstractions, which represent 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.