research-article

Synthesizing parallel graph programs via automated planning

Authors:

Dimitrios Prountzos,

Roman Manevich,

Keshav PingaliAuthors Info & Claims

PLDI '15: Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 533 - 544

https://doi.org/10.1145/2737924.2737953

Published: 03 June 2015 Publication History

Abstract

We describe a system that uses automated planning to synthesize correct and efficient parallel graph programs from high-level algorithmic specifications. Automated planning allows us to use constraints to declaratively encode program transformations such as scheduling, implementation selection, and insertion of synchronization. Each plan emitted by the planner satisfies all constraints simultaneously, and corresponds to a composition of these transformations. In this way, we obtain an integrated compilation approach for a very challenging problem domain. We have used this system to synthesize parallel programs for four graph problems: triangle counting, maximal independent set computation, preflow-push maxflow, and connected components. Experiments on a variety of inputs show that the synthesized implementations perform competitively with hand-written, highly-tuned code.

References

[1]

A. Aho, R. Sethi, and J. Ullman. Compilers: principles, techniques, and tools. Addison Wesley, 1986.

Digital Library

[2]

G. Barthe, J. M. Crespo, S. Gulwani, C. Kunz, and M. Marron. From relational verification to SIMD loop synthesis. PPoPP ’13, 2013.

Digital Library

[3]

A. J. C. Bik and H. A. G. Wijshoff. Compilation techniques for sparse matrix computations. In ICS, 1993.

Digital Library

[4]

G. E. Blelloch, J. T. Fineman, P. B. Gibbons, and J. Shun. Internally deterministic parallel algorithms can be fast. PPoPP ’12, 2012.

Digital Library

[5]

S. Cherem, T. Chilimbi, and S. Gulwani. Inferring locks for atomic sections. In PLDI. ACM, 2008. ISBN 978-1-59593-860-2.

Digital Library

[6]

G. Cong, G. Almasi, and V. Saraswat. Fast pgas connected components algorithms. PGAS ’09. ACM, 2009.

Digital Library

[7]

T. Cormen, C. Leiserson, R. Rivest, and C. Stein, editors. Introduction to Algorithms. MIT Press, 2001.

Digital Library

[8]

M. Eriksson and C. Kessler. Integrated code generation for loops. ACM Trans. Embed. Comput. Syst., 11S(1), June 2012.

Digital Library

[9]

R. E. Fikes and N. J. Nilsson. Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, 1971.

Digital Library

[10]

S. Gulwani, S. Jha, A. Tiwari, and R. Venkatesan. Synthesis of loopfree programs. PLDI ’11, 2011.

Digital Library

[11]

P. Hawkins, A. Aiken, K. Fisher, M. Rinard, and M. Sagiv. Concurrent data representation synthesis. In PLDI, 2012.

Digital Library

[12]

M. Herlihy and E. Koskinen. Transactional boosting: a methodology for highly-concurrent transactional objects. In PPoPP. ACM, 2008.

Digital Library

[13]

S. Itzhaky, S. Gulwani, N. Immerman, and M. Sagiv. A simple inductive synthesis methodology and its applications. In OOPSLA, 2010.

Digital Library

[14]

J. F. JaJa. An introduction to parallel algorithms. Addison Wesley, 1992.

Digital Library

[15]

T. A. Johnson and R. Eigenmann. Context-sensitive domainindependent algorithm composition and selection. PLDI ’06, 2006.

Digital Library

[16]

R. Joshi, G. Nelson, and K. Randall. Denali: A goal-directed superoptimizer. In PLDI, 2002.

Digital Library

[17]

R. Joshi, G. Nelson, and Y. Zhou. Denali: A practical algorithm for generating optimal code. ACM Trans. Program. Lang. Syst., 28(6), 2006.

Digital Library

[18]

H. A. Kautz, B. Selman, et al. Planning as satisfiability. ECAI, 1992.

Digital Library

[19]

H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? WWW ’10, 2010.

Digital Library

[20]

Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new parallel framework for machine learning. In UAI, 2010.

Digital Library

[21]

H. Massalin. Superoptimizer: A look at the smallest program. In ASPLOS, 1987.

[22]

B. McCloskey, F. Zhou, D. Gay, and E. Brewer. Autolocker: synchronization inference for atomic sections. In POPL. ACM, 2006.

Digital Library

[23]

L. A. Meyerovich, M. E. Torok, E. Atkinson, and R. Bodik. Parallel schedule synthesis for attribute grammars. PPoPP ’13, 2013.

Digital Library

[24]

D. Nguyen, A. Lenharth, and K. Pingali. A lightweight infrastructure for graph analytics. In SOSP, 2013.

Digital Library

[25]

K. Pingali, D. Nguyen, M. Kulkarni, M. Burtscher, M. A. Hassaan, R. Kaleem, T. H. Lee, A. Lenharth, R. Manevich, M. Méndez-Lojo, D. Prountzos, and X. Sui. The TAO of parallelism in algorithms. In PLDI, 2011.

Digital Library

[26]

D. Prountzos, R. Manevich, and K. Pingali. Elixir: A system for synthesizing concurrent graph programs. OOPSLA, 2012.

Digital Library

[27]

T. Rompf, A. K. Sujeeth, N. Amin, K. J. Brown, V. Jovanovic, H. Lee, M. Jonnalagedda, K. Olukotun, and M. Odersky. Optimizing data structures in high-level programs: New directions for extensible compilers based on staging. In POPL, 2013.

Digital Library

[28]

N. Satish, N. Sundaram, M. M. A. Patwary, J. Seo, J. Park, M. A. Hassaan, S. Sengupta, Z. Yin, and P. Dubey. Navigating the maze of graph analytics frameworks using massive graph datasets. SIGMOD, 2014.

Digital Library

[29]

T. Schank. Algorithmic Aspects of Triangle-Based Network Analysis. PhD thesis, Universität Karlsruhe, 2007.

[30]

E. Schkufza, R. Sharma, and A. Aiken. Stochastic superoptimization. ASPLOS ’13, 2013.

Digital Library

[31]

Y. Shiloach and U. Vishkin. An o(log n) parallel connectivity algorithm. J. Algorithms, 3(1):57–67, 1982.

[32]

J. Shun and G. E. Blelloch. Ligra: A lightweight graph processing framework for shared memory. PPoPP ’13, 2013.

Digital Library

[33]

A. Solar-Lezama, C. Jones, and R. Bodik. Sketching concurrent data structures. In PLDI, 2008.

Digital Library

[34]

R. Tate, M. Stepp, Z. Tatlock, and S. Lerner. Equality saturation: A new approach to optimization. In POPL, 2009.

Digital Library

[35]

M. Vechev and E. Yahav. Deriving linearizable fine-grained concurrent objects. In PLDI, 2008.

Digital Library

[36]

M. Vechev, E. Yahav, and G. Yorsh. Abstraction-guided synthesis of synchronization. In POPL, 2010.

Digital Library

Cited By

IWASAKI HEMOTO KMORIHATA AMATSUZAKI KHU Z(2022)Fregel: a functional domain-specific language for vertex-centric large-scale graph processingJournal of Functional Programming10.1017/S095679682100027732Online publication date: 20-Jan-2022
https://doi.org/10.1017/S0956796821000277
Pandey PWheatman BXu HBuluc ALi GLi ZIdreos SSrivastava D(2021)Terrace: A Hierarchical Graph Container for Skewed Dynamic GraphsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457313(1372-1385)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457313
Wheatman BBurns R(2021)Streaming Sparse Graphs using Efficient Dynamic Sets2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671836(284-294)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671836
Show More Cited By

Index Terms

Synthesizing parallel graph programs via automated planning
1. Computing methodologies
  1. Concurrent computing methodologies
    1. Concurrent programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Concurrent programming languages

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PLDI '15

We describe a system that uses automated planning to synthesize correct and efficient parallel graph programs from high-level algorithmic specifications. Automated planning allows us to use constraints to declaratively encode program transformations ...

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Published In

cover image ACM Conferences

PLDI '15: Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2015

630 pages

ISBN:9781450334686

DOI:10.1145/2737924

General Chair:
David Grove
IBM Research, USA
,
Program Chair:
Steve Blackburn
Australian National University, Australia

ACM SIGPLAN Notices Volume 50, Issue 6
PLDI '15
June 2015
630 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2813885
Editor:
Andy Gill
University of Kansas, Lawrence, KS
Issue’s Table of Contents

Copyright © 2015 ACM.

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Published: 03 June 2015

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PLDI '15: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 13 - 17, 2015

OR, Portland, USA

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Overall Acceptance Rate 406 of 2,067 submissions, 20%

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Cited By

IWASAKI HEMOTO KMORIHATA AMATSUZAKI KHU Z(2022)Fregel: a functional domain-specific language for vertex-centric large-scale graph processingJournal of Functional Programming10.1017/S095679682100027732Online publication date: 20-Jan-2022
https://doi.org/10.1017/S0956796821000277
Pandey PWheatman BXu HBuluc ALi GLi ZIdreos SSrivastava D(2021)Terrace: A Hierarchical Graph Container for Skewed Dynamic GraphsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457313(1372-1385)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457313
Wheatman BBurns R(2021)Streaming Sparse Graphs using Efficient Dynamic Sets2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671836(284-294)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671836
Dhulipala LBlelloch GShun JMcKinley KFisher K(2019)Low-latency graph streaming using compressed purely-functional treesProceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3314221.3314598(918-934)Online publication date: 8-Jun-2019
https://dl.acm.org/doi/10.1145/3314221.3314598
Henriksen TThorøe FElsman MOancea CHollingsworth JKeidar I(2019)Incremental flattening for nested data parallelismProceedings of the 24th Symposium on Principles and Practice of Parallel Programming10.1145/3293883.3295707(53-67)Online publication date: 16-Feb-2019
https://dl.acm.org/doi/10.1145/3293883.3295707
Zhang YYang MBaghdadi RKamil SShun JAmarasinghe S(2018)GraphIt: a high-performance graph DSLProceedings of the ACM on Programming Languages10.1145/32764912:OOPSLA(1-30)Online publication date: 24-Oct-2018
https://dl.acm.org/doi/10.1145/3276491
Gill GDathathri RHoang LLenharth APingali K(2018)Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous PlatformsEuro-Par 2018: Parallel Processing10.1007/978-3-319-96983-1_18(249-264)Online publication date: 1-Aug-2018
https://doi.org/10.1007/978-3-319-96983-1_18
Morihata AEmoto KMatsuzaki KHu ZIwasaki H(2018)Optimizing Declarative Parallel Distributed Graph Processing by Using Constraint SolversFunctional and Logic Programming10.1007/978-3-319-90686-7_11(166-181)Online publication date: 24-Apr-2018
https://doi.org/10.1007/978-3-319-90686-7_11
Shashidhar GNasre R(2017)LightHouse: An Automatic Code Generator for Graph Algorithms on GPUsLanguages and Compilers for Parallel Computing10.1007/978-3-319-52709-3_18(235-249)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-52709-3_18
Jeffrey MSubramanian SAbeydeera MEmer JSanchez DHsu WYang CLipasti MLee H(2016)Data-centric execution of speculative parallel programsThe 49th Annual IEEE/ACM International Symposium on Microarchitecture10.5555/3195638.3195644(1-13)Online publication date: 15-Oct-2016
https://dl.acm.org/doi/10.5555/3195638.3195644
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