research-article

Green-Marl: a DSL for easy and efficient graph analysis

Authors:

Kunle OlukotunAuthors Info & Claims

ASPLOS XVII: Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems

Pages 349 - 362

https://doi.org/10.1145/2150976.2151013

Published: 03 March 2012 Publication History

Abstract

The increasing importance of graph-data based applications is fueling the need for highly efficient and parallel implementations of graph analysis software. In this paper we describe Green-Marl, a domain-specific language (DSL) whose high level language constructs allow developers to describe their graph analysis algorithms intuitively, but expose the data-level parallelism inherent in the algorithms. We also present our Green-Marl compiler which translates high-level algorithmic description written in Green-Marl into an efficient C++ implementation by exploiting this exposed data-level parallelism. Furthermore, our Green-Marl compiler applies a set of optimizations that take advantage of the high-level semantic knowledge encoded in the Green-Marl DSL. We demonstrate that graph analysis algorithms can be written very intuitively with Green-Marl through some examples, and our experimental results show that the compiler-generated implementation out of such descriptions performs as well as or better than highly-tuned hand-coded implementations.

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Cited By

Behera NKumar ARajadurai T ENitish SM RNasre R(2024)StarPlat: A Versatile DSL for Graph AnalyticsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104967(104967)Online publication date: Aug-2024
https://doi.org/10.1016/j.jpdc.2024.104967
Firmli SChiadmi D(2023)A Scalable Data Structure for Efficient Graph Analytics and In-Place MutationsData10.3390/data81101668:11(166)Online publication date: 3-Nov-2023
https://doi.org/10.3390/data8110166
Lee ENoh SSeo J(2023)SageProceedings of the VLDB Endowment10.14778/3565838.356584415:13(3897-3910)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.14778/3565838.3565844
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Index Terms

Green-Marl: a DSL for easy and efficient graph analysis
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

ASPLOS XVII: Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems

March 2012

476 pages

ISBN:9781450307598

DOI:10.1145/2150976

General Chair:
Tim Harris
Microsoft Research
,
Program Chair:
Michael L. Scott
University of Rochester

ACM SIGARCH Computer Architecture News Volume 40, Issue 1
ASPLOS '12
March 2012
453 pages
ISSN:0163-5964
DOI:10.1145/2189750
Issue’s Table of Contents
ACM SIGPLAN Notices Volume 47, Issue 4
ASPLOS '12
April 2012
453 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2248487
Issue’s Table of Contents

Copyright © 2012 ACM.

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Published: 03 March 2012

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ASPLOS'12: Seventeenth International Conference on Architectural Support for Programming Languages and Operating Systems

March 3 - 7, 2012

London, England, UK

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Cited By

Behera NKumar ARajadurai T ENitish SM RNasre R(2024)StarPlat: A Versatile DSL for Graph AnalyticsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104967(104967)Online publication date: Aug-2024
https://doi.org/10.1016/j.jpdc.2024.104967
Firmli SChiadmi D(2023)A Scalable Data Structure for Efficient Graph Analytics and In-Place MutationsData10.3390/data81101668:11(166)Online publication date: 3-Nov-2023
https://doi.org/10.3390/data8110166
Lee ENoh SSeo J(2023)SageProceedings of the VLDB Endowment10.14778/3565838.356584415:13(3897-3910)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.14778/3565838.3565844
Zhou YLeng JSong YLu SWang MLi CGuo MShen WLi YLin WLiu XWu HAamodt TJerger NSwift M(2023)uGrapher: High-Performance Graph Operator Computation via Unified Abstraction for Graph Neural NetworksProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575723(878-891)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3575693.3575723
Chen YChung Y(2023)An Unequal Caching Strategy for Shared-Memory Graph AnalyticsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.321888534:3(955-967)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TPDS.2022.3218885
Peng ZAshraf RGuo LTian RKestor G(2023)Automatic Code Generation for High-Performance Graph Algorithms2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT)10.1109/PACT58117.2023.00010(14-26)Online publication date: 21-Oct-2023
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Boukham HWachsmuth GDwars MChiadmi DFischer BBurgueño LCazzola W(2022)A Multi-target, Multi-paradigm DSL Compiler for Algorithmic Graph ProcessingProceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering10.1145/3567512.3567513(2-15)Online publication date: 29-Nov-2022
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Kang SLee KShin K(2022)Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00219(2319-2332)Online publication date: May-2022
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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
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Eedi HKarra SPeri SRanabothu NUtkoor R(2022)An Improved/Optimized Practical Non-Blocking PageRank Algorithm for Massive Graphs*International Journal of Parallel Programming10.1007/s10766-022-00725-650:3-4(381-404)Online publication date: 26-Mar-2022
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