Article

Online Phase Detection Algorithms

Authors:

Priya Nagpurkar,

Chandra Krintz,

Peter F. Sweeney,

V. T. RajanAuthors Info & Claims

CGO '06: Proceedings of the International Symposium on Code Generation and Optimization

Pages 111 - 123

https://doi.org/10.1109/CGO.2006.26

Published: 26 March 2006 Publication History

Abstract

Today's virtual machines (VMs) dynamically optimize an application as it is executing, often employing optimizations that are specialized for the current execution profile. An online phase detector determines when an executing program is in a stable period of program execution (a phase) or is in transition. A VM using an online phase detector can apply specialized optimizations during a phase or reconsider optimization decisions between phases. Unfortunately, extant approaches to detecting phase behavior rely on either offline profiling, hardware support, or are targeted toward a particular optimization. In this work, we focus on the enabling technology of online phase detection. More specifically, we contribute (a) a novel framework for online phase detection, (b) multiple instantiations of the framework that produce novel online phase detection algorithms, (c) a novel client- and machine-independent baseline methodology for evaluating the accuracy of an online phase detector, (d) a metric to compare online detectors to this baseline, and (e) a detailed empirical evaluation, using Java applications, of the accuracy of the numerous phase detectors.

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Cited By

Gangadhar PHota ARao MRao V(2019)Performance of Memory Virtualization Using Global Memory Resource BalancingInternational Journal of Cloud Applications and Computing10.4018/IJCAC.20190101029:1(16-32)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJCAC.2019010102
Effler TKammerdiener BJantz MSengupta SKulkarni PDoshi KJones T(2019)Evaluating the effectiveness of program data features for guiding memory managementProceedings of the International Symposium on Memory Systems10.1145/3357526.3357537(383-395)Online publication date: 30-Sep-2019
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Chiu MMoss ETilevich EMössenböck H(2018)Run-time program-specific phase prediction for python programsProceedings of the 15th International Conference on Managed Languages & Runtimes10.1145/3237009.3237011(1-10)Online publication date: 12-Sep-2018
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Index Terms

Online Phase Detection Algorithms
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Runtime environments
    2. General programming languages
      1. Language types

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cover image ACM Conferences

CGO '06: Proceedings of the International Symposium on Code Generation and Optimization

March 2006

347 pages

ISBN:0769524990

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Publisher

IEEE Computer Society

United States

Publication History

Published: 26 March 2006

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CGO06

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CGO06: 4th Annual IEEE / ACM International Symposium on Code Generation and Optimization

March 26 - 29, 2006

Acceptance Rates

CGO '06 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 312 of 1,061 submissions, 29%

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Cited By

Gangadhar PHota ARao MRao V(2019)Performance of Memory Virtualization Using Global Memory Resource BalancingInternational Journal of Cloud Applications and Computing10.4018/IJCAC.20190101029:1(16-32)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJCAC.2019010102
Effler TKammerdiener BJantz MSengupta SKulkarni PDoshi KJones T(2019)Evaluating the effectiveness of program data features for guiding memory managementProceedings of the International Symposium on Memory Systems10.1145/3357526.3357537(383-395)Online publication date: 30-Sep-2019
https://dl.acm.org/doi/10.1145/3357526.3357537
Chiu MMoss ETilevich EMössenböck H(2018)Run-time program-specific phase prediction for python programsProceedings of the 15th International Conference on Managed Languages & Runtimes10.1145/3237009.3237011(1-10)Online publication date: 12-Sep-2018
https://dl.acm.org/doi/10.1145/3237009.3237011
De Sensi DTorquati MDanelutto M(2016)A Reconfiguration Algorithm for Power-Aware Parallel ApplicationsACM Transactions on Architecture and Code Optimization10.1145/300405413:4(1-25)Online publication date: 2-Dec-2016
https://dl.acm.org/doi/10.1145/3004054
Chiu MMarlin BMoss EZheng YBinder WTůma P(2016)Real-Time Program-Specific Phase Change Detection for Java ProgramsProceedings of the 13th International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools10.1145/2972206.2972221(1-11)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.1145/2972206.2972221
Fu JJin GZhang LWang JPalermo GFeo JTumeo AFranke H(2016)CAOSProceedings of the ACM International Conference on Computing Frontiers10.1145/2903150.2903151(110-118)Online publication date: 16-May-2016
https://dl.acm.org/doi/10.1145/2903150.2903151
Stadler LDuboscq GMössenböck HWürthinger TGrove D(2012)Compilation queuing and graph caching for dynamic compilersProceedings of the sixth ACM workshop on Virtual machines and intermediate languages10.1145/2414740.2414750(49-58)Online publication date: 21-Oct-2012
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Luo YZhai A(2012)Dynamically dispatching speculative threads to improve sequential executionACM Transactions on Architecture and Code Optimization10.1145/2355585.23555869:3(1-31)Online publication date: 5-Oct-2012
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Sembrant ABlack-Schaffer DHagersten EEidt CHoller ASrinivasan UAmarasinghe S(2012)Phase guided profiling for fast cache modelingProceedings of the Tenth International Symposium on Code Generation and Optimization10.1145/2259016.2259040(175-185)Online publication date: 31-Mar-2012
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Simão JVeiga L(2012)VM Economics for Java Cloud ComputingProceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)10.1109/CCGrid.2012.121(723-728)Online publication date: 13-May-2012
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