research-article

P3: partitioned path profiling

Authors:

Mohammed Afraz,

Diptikalyan Saha,

Aditya KanadeAuthors Info & Claims

ESEC/FSE 2015: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

Pages 485 - 495

https://doi.org/10.1145/2786805.2786868

Published: 30 August 2015 Publication History

Abstract

Acyclic path profile is an abstraction of dynamic control flow paths of procedures and has been found to be useful in a wide spectrum of activities. Unfortunately, the runtime overhead of obtaining such a profile can be high, limiting its use in practice. In this paper, we present partitioned path profiling (P3) which runs K copies of the program in parallel, each with the same input but on a separate core, and collects the profile only for a subset of intra-procedural paths in each copy, thereby, distributing the overhead of profiling. P3 identifies “profitable” procedures and assigns disjoint subsets of paths of a profitable procedure to different copies for profiling. To obtain exact execution frequencies of a subset of paths, we design a new algorithm, called PSPP. All paths of an unprofitable procedure are assigned to the same copy. P3 uses the classic Ball-Larus algorithm for profiling unprofitable procedures. Further, P3 attempts to evenly distribute the profiling overhead across the copies. To the best of our knowledge, P3 is the first algorithm for parallel path profiling. We have applied P3 to profile several programs in the SPEC 2006 benchmark. Compared to sequential profiling, P3 substantially reduced the runtime overhead on these programs averaged across all benchmarks. The reduction was 23%, 43% and 56% on average for 2, 4 and 8 cores respectively. P3 also performed better than a coarse-grained approach that treats all procedures as unprofitable and distributes them across available cores. For 2 cores, the profiling overhead of P3 was on average 5% less compared to the coarse-grained approach across these programs. For 4 and 8 cores, it was respectively 18% and 25% less.

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Cited By

Yilmaz BWerner FPark SUgurlu EJorgensen EPrvulovic MZajic A(2023)MarCNNet: A Markovian Convolutional Neural Network for Malware Detection and Monitoring Multi-Core SystemsIEEE Transactions on Computers10.1109/TC.2022.318452072:4(1122-1135)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TC.2022.3184520
Callan RBehrang FZajic APrvulovic MOrso AZeller ARoychoudhury A(2016)Zero-overhead profiling via EM emanationsProceedings of the 25th International Symposium on Software Testing and Analysis10.1145/2931037.2931065(401-412)Online publication date: 18-Jul-2016
https://dl.acm.org/doi/10.1145/2931037.2931065
Derbeko PDolev SGudes EUllman J(2016)Concise essence-preserving big data representation2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7841033(3662-3665)Online publication date: Dec-2016
https://doi.org/10.1109/BigData.2016.7841033

Index Terms

P3: partitioned path profiling
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
2. Theory of computation
  1. Semantics and reasoning
    1. Program semantics

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cover image ACM Conferences

ESEC/FSE 2015: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

August 2015

1068 pages

ISBN:9781450336758

DOI:10.1145/2786805

General Chair:
Elisabetta Di Nitto
Politecnico di Milano, Italy
,
Program Chairs:
Mark Harman
University College London, UK
,
Patrick Heymans
University of Namur, Belgium

Copyright © 2015 ACM.

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New York, NY, United States

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Published: 30 August 2015

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ESEC/FSE'15: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

August 30 - September 4, 2015

Bergamo, Italy

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Cited By

Yilmaz BWerner FPark SUgurlu EJorgensen EPrvulovic MZajic A(2023)MarCNNet: A Markovian Convolutional Neural Network for Malware Detection and Monitoring Multi-Core SystemsIEEE Transactions on Computers10.1109/TC.2022.318452072:4(1122-1135)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TC.2022.3184520
Callan RBehrang FZajic APrvulovic MOrso AZeller ARoychoudhury A(2016)Zero-overhead profiling via EM emanationsProceedings of the 25th International Symposium on Software Testing and Analysis10.1145/2931037.2931065(401-412)Online publication date: 18-Jul-2016
https://dl.acm.org/doi/10.1145/2931037.2931065
Derbeko PDolev SGudes EUllman J(2016)Concise essence-preserving big data representation2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7841033(3662-3665)Online publication date: Dec-2016
https://doi.org/10.1109/BigData.2016.7841033

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