article

A framework for reducing the cost of instrumented code

Authors:

Matthew Arnold,

Barbara G. RyderAuthors Info & Claims

ACM SIGPLAN Notices, Volume 36, Issue 5

Pages 168 - 179

https://doi.org/10.1145/381694.378832

Published: 01 May 2001 Publication History

Abstract

Instrumenting code to collect profiling information can cause substantial execution overhead. This overhead makes instrumentation difficult to perform at runtime, often preventing many known offline feedback-directed optimizations from being used in online systems. This paper presents a general framework for performing instrumentation sampling to reduce the overhead of previously expensive instrumentation. The framework is simple and effective, using code-duplication and counter-based sampling to allow switching between instrumented and non-instrumented code.

Our framework does not rely on any hardware or operating system support, yet provides a high frequency sample rate that is tunable, allowing the tradeoff between overhead and accuracy to be adjusted easily at runtime. Experimental results are presented to validate that our technique can collect accurate profiles (93-98% overlap with a perfect profile) with low overhead (averaging 6% total overhead with a naive implementation). A Jalape~ no-specific optimization is also presented that reduces overhead further, resulting in an average total overhead of 3%.

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Cited By

Vukasovic MProkopec A(2023)Exploiting Partially Context-sensitive Profiles to Improve Performance of Hot CodeACM Transactions on Programming Languages and Systems10.1145/361293745:4(1-64)Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1145/3612937
Zuo ZNiu XZhang SFang LKhoo SLu SSun CXu G(2023)Toward More Efficient Statistical Debugging with Abstraction RefinementACM Transactions on Software Engineering and Methodology10.1145/354479032:2(1-38)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1145/3544790
Maruthamuthu RDhabliya DPriyadarshini G KAbbas ABarno Akumar V(2023)Advancements in Compiler Design and Optimization TechniquesE3S Web of Conferences10.1051/e3sconf/202339904047399(04047)Online publication date: 12-Jul-2023
https://doi.org/10.1051/e3sconf/202339904047
Show More Cited By

Index Terms

A framework for reducing the cost of instrumented code
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Runtime environments
      2. Source code generation
    2. General programming languages
      1. Language features
        Frameworks

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Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 36, Issue 5

May 2001

330 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/381694

Issue’s Table of Contents

PLDI '01: Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
June 2001
331 pages
ISBN:1581134142
DOI:10.1145/378795
Chairmen:
Michael Burke
IBM T.J. Watson Research Center
,
Mary Lou Soffa
Univ. of Pittsburgh

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 01 May 2001

Published in SIGPLAN Volume 36, Issue 5

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https://dl.acm.org/doi/10.1145/3612937
Zuo ZNiu XZhang SFang LKhoo SLu SSun CXu G(2023)Toward More Efficient Statistical Debugging with Abstraction RefinementACM Transactions on Software Engineering and Methodology10.1145/354479032:2(1-38)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1145/3544790
Maruthamuthu RDhabliya DPriyadarshini G KAbbas ABarno Akumar V(2023)Advancements in Compiler Design and Optimization TechniquesE3S Web of Conferences10.1051/e3sconf/202339904047399(04047)Online publication date: 12-Jul-2023
https://doi.org/10.1051/e3sconf/202339904047
Wang YZheng Q(2021)A Logging Overhead Optimization Method Based on Anomaly Detection ModelHuman Centered Computing10.1007/978-3-030-70626-5_37(349-359)Online publication date: 12-Mar-2021
https://doi.org/10.1007/978-3-030-70626-5_37
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Bhattacharyya AAmza C(2019)ADLER: Adaptive Sampling for Precise MonitoringLanguages and Compilers for Parallel Computing10.1007/978-3-030-35225-7_7(86-100)Online publication date: 15-Nov-2019
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