Article

Optimistic parallelism requires abstractions

Authors:

Milind Kulkarni,

Keshav Pingali,

Ganesh Ramanarayanan,

L. Paul ChewAuthors Info & Claims

PLDI '07: Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 211 - 222

https://doi.org/10.1145/1250734.1250759

Published: 10 June 2007 Publication History

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.

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Index Terms

Optimistic parallelism requires abstractions
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Parallel programming languages

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cover image ACM Conferences

PLDI '07: Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2007

508 pages

ISBN:9781595936332

DOI:10.1145/1250734

General Chair:
Jeanne Ferrante
University of California, San Diego, USA
,
Program Chair:
Kathryn S. McKinley
University of Texas at Austin, USA

ACM SIGPLAN Notices Volume 42, Issue 6
Proceedings of the 2007 PLDI conference
June 2007
491 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/1273442
Issue’s Table of Contents

Copyright © 2007 ACM.

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Published: 10 June 2007

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June 10 - 13, 2007

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https://dl.acm.org/doi/10.1145/3626183.3659966
Zhang GPosluns GJeffrey MAgrawal KPetrank E(2024)Multi Bucket Queues: Efficient Concurrent Priority SchedulingProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3659962(113-124)Online publication date: 17-Jun-2024
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Łoś MPaszyński M(2024)Parallel shared-memory open-source code for simulations of transient problems using isogeometric analysis, implicit direction splitting and residual minimization (IGA-ADS-RM)Advances in Engineering Software10.1016/j.advengsoft.2024.103723196(103723)Online publication date: Oct-2024
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