Optimistic parallelism requires abstractions

  • Authors:

  • Milind Kulkarni;Keshav Pingali;Bruce Walter;Ganesh Ramanarayanan;Kavita Bala;L. Paul Chew

  • Affiliations:

  • University of Texas, Austin, TX;University of Texas, Austin, TX;Cornell University, Ithaca, TX;Cornell University, Ithaca, TX;Cornell University, Ithaca, TX;Cornell University, Ithaca, TX

  • Venue:
  • Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
  • Year:
  • 2007

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Abstract

Irregular applications, which manipulate large, pointer-based data structures like graphs, are difficult to parallelize manually. Automatic tools and techniques such as restructuring compilers and run-time speculative execution have failed to uncover much parallelism in these applications, in spite of a lot of effort by the research community. These difficulties have even led some researchers to wonder if there is any coarse-grain parallelism worth exploiting in irregular applications. In this paper, we describe two real-world irregular applications: a Delaunay mesh refinement application and a graphics application thatperforms agglomerative clustering. By studying the algorithms and data structures used in theseapplications, we show that there is substantial coarse-grain, data parallelism in these applications, but that this parallelism is very dependent on the input data and therefore cannot be uncoveredby compiler analysis. In principle, optimistic techniques such asthread-level speculation can be used to uncover this parallelism, but we argue that current implementations cannot accomplish thisbecause they do not use the proper abstractions for the data structuresin these programs. These insights have informed our design of the Galois system, an object-based optimistic parallelization system for irregular applications. There are three main aspects to Galois: (1) a small number of syntactic constructs for packaging optimistic parallelism as iteration over ordered and unordered sets, (2)assertions about methods in class libraries, and (3) a runtime scheme for detecting and recovering from potentially unsafe accesses to shared memory made by an optimistic computation. We show that Delaunay mesh generation and agglomerative clustering can be parallelized in a straight-forward way using the Galois approach, and we present experimental measurements to show that this approach is practical. These results suggest that Galois is a practical approach to exploiting data parallelismin irregular programs.