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Analysis and Optimization of Task Granularity on the Java Virtual Machine

Authors:

Eduardo Rosales,

Walter BinderAuthors Info & Claims

ACM Transactions on Programming Languages and Systems (TOPLAS), Volume 41, Issue 3

Article No.: 19, Pages 1 - 47

https://doi.org/10.1145/3338497

Published: 16 July 2019 Publication History

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Abstract

Task granularity, i.e., the amount of work performed by parallel tasks, is a key performance attribute of parallel applications. On the one hand, fine-grained tasks (i.e., small tasks carrying out few computations) may introduce considerable parallelization overheads. On the other hand, coarse-grained tasks (i.e., large tasks performing substantial computations) may not fully utilize the available CPU cores, leading to missed parallelization opportunities. In this article, we provide a better understanding of task granularity for task-parallel applications running on a single Java Virtual Machine in a shared-memory multicore. We present a new methodology to accurately and efficiently collect the granularity of each executed task, implemented in a novel profiler (available open-source) that collects carefully selected metrics from the whole system stack with low overhead, and helps developers locate performance and scalability problems. We analyze task granularity in the DaCapo, ScalaBench, and Spark Perf benchmark suites, revealing inefficiencies related to fine-grained and coarse-grained tasks in several applications. We demonstrate that the collected task-granularity profiles are actionable by optimizing task granularity in several applications, achieving speedups up to a factor of 5.90×. Our results highlight the importance of analyzing and optimizing task granularity on the Java Virtual Machine.

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Cited By

Löff JSchiavio FRosà ABasso MBinder WBalsamo SKnottenbelt WAbad CShang W(2024)Vectorized Intrinsics Can Be Replaced with Pure Java Code without Impairing Steady-State PerformanceProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645051(14-24)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645051
Basso MProkopec ARosà ABinder W(2023)Optimization-Aware Compiler-Level Event ProfilingACM Transactions on Programming Languages and Systems10.1145/359147345:2(1-50)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3591473
Li BSu PChabbi MJiao SLiu XDubach CBruening DHardekopf B(2023)DJXPerf: Identifying Memory Inefficiencies via Object-Centric Profiling for JavaProceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization10.1145/3579990.3580010(81-94)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3579990.3580010
Show More Cited By

Index Terms

Analysis and Optimization of Task Granularity on the Java Virtual Machine
1. General and reference
  1. Cross-computing tools and techniques
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance
    2. Software functional properties
      1. Formal methods
        Dynamic analysis

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cover image ACM Transactions on Programming Languages and Systems

ACM Transactions on Programming Languages and Systems Volume 41, Issue 3

September 2019

278 pages

ISSN:0164-0925

EISSN:1558-4593

DOI:10.1145/3343145

Editor:
Andrew Myers
Cornell University, USA

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Copyright © 2019 ACM.

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Publication History

Published: 16 July 2019

Accepted: 01 May 2019

Revised: 01 March 2019

Received: 01 August 2018

Published in TOPLAS Volume 41, Issue 3

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Cited By

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https://dl.acm.org/doi/10.1145/3629526.3645051
Basso MProkopec ARosà ABinder W(2023)Optimization-Aware Compiler-Level Event ProfilingACM Transactions on Programming Languages and Systems10.1145/359147345:2(1-50)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3591473
Li BSu PChabbi MJiao SLiu XDubach CBruening DHardekopf B(2023)DJXPerf: Identifying Memory Inefficiencies via Object-Centric Profiling for JavaProceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization10.1145/3579990.3580010(81-94)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3579990.3580010
Álvarez DBeltran V(2023)Optimizing Iterative Data-flow Scientific Applications using Directed Cyclic GraphsIEEE Access10.1109/ACCESS.2023.3269902(1-1)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269902
Rosales EBasso MRosà ABinder W(2023)Large‐scale characterization of Java streamsSoftware: Practice and Experience10.1002/spe.321353:9(1763-1792)Online publication date: 5-Jun-2023
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Basso MRosales ESchiavio FRosà ABinder W(2022)Accurate Fork-Join Profiling on the Java Virtual MachineEuro-Par 2022: Parallel Processing10.1007/978-3-031-12597-3_3(35-50)Online publication date: 22-Aug-2022
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Rosà ABinder W(2020): A Profiler Suite for Parallel Applications on the Java Virtual MachineProgramming Languages and Systems10.1007/978-3-030-64437-6_19(364-372)Online publication date: 30-Nov-2020
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