research-article

Inferred call path profiling

Authors:

Todd Mytkowicz,

Devin Coughlin,

Amer DiwanAuthors Info & Claims

ACM SIGPLAN Notices, Volume 44, Issue 10

Pages 175 - 190

https://doi.org/10.1145/1639949.1640102

Published: 25 October 2009 Publication History

Abstract

Prior work has found call path profiles to be useful for optimizers and programmer-productivity tools. Unfortunately, previous approaches for collecting path profiles are expensive: they need to either execute additional instructions (to track calls and returns) or they need to walk the stack. The state-of-the-art techniques for call path profiling slow down the program by 7% (for C programs) and 20% (for Java programs). This paper describes an innovative technique that collects minimal information from the running program and later (offline) infers the full call paths from this information.

The key insight behind our approach is that readily available information during program execution - the height of the call stack and the identity of the current executing function - are good indicators of calling context. We call this pair a context identifier. Because more than one call path may have the same context identifier, we show how to disambiguate context identifiers by changing the sizes of function activation records. This disambiguation has no overhead in terms of executed instructions.

We evaluate our approach on the SPEC CPU 2006 C++ and C benchmarks. We show that collecting context identifiers slows down programs by 0.17% (geometric mean). We can map these context identifiers to the correct unique call path 80% of the time for C++ programs and 95% of the time for C programs.

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https://dl.acm.org/doi/10.1145/3591195.3595275
Zhou KAnderson JMeng XMellor-Crummey JRauchwerger LCameron KNikolopoulos DPnevmatikatos D(2022)Low overhead and context sensitive profiling of GPU-accelerated applicationsProceedings of the 36th ACM International Conference on Supercomputing10.1145/3524059.3532388(1-13)Online publication date: 28-Jun-2022
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Maas MAndersen DIsard MJavanmard MMcKinley KRaffel C(2024)Combining Machine Learning and Lifetime-Based Resource Management for Memory Allocation and BeyondCommunications of the ACM10.1145/361101867:4(87-96)Online publication date: 25-Mar-2024
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Index Terms

Inferred call path profiling
1. Hardware
  1. Hardware validation

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Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 44, Issue 10

OOPSLA '09

October 2009

554 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/1639949

Issue’s Table of Contents

OOPSLA '09: Proceedings of the 24th ACM SIGPLAN conference on Object oriented programming systems languages and applications
October 2009
590 pages
ISBN:9781605587660
DOI:10.1145/1640089
General Chair:
Shail Arora
Adayana, Inc.
,
Program Chair:
Gary Leavens
University of Central Florida

Copyright © 2009 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

New York, NY, United States

Publication History

Published: 25 October 2009

Published in SIGPLAN Volume 44, Issue 10

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Cited By

Navasca CMaas MManiatis PLim HXu GBlackburn SPetrank E(2023)Predicting Dynamic Properties of Heap Allocations using Neural Networks Trained on Static Code: An Intellectual AbstractProceedings of the 2023 ACM SIGPLAN International Symposium on Memory Management10.1145/3591195.3595275(43-57)Online publication date: 6-Jun-2023
https://dl.acm.org/doi/10.1145/3591195.3595275
Zhou KAnderson JMeng XMellor-Crummey JRauchwerger LCameron KNikolopoulos DPnevmatikatos D(2022)Low overhead and context sensitive profiling of GPU-accelerated applicationsProceedings of the 36th ACM International Conference on Supercomputing10.1145/3524059.3532388(1-13)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3524059.3532388
Maas MAndersen DIsard MJavanmard MMcKinley KRaffel C(2024)Combining Machine Learning and Lifetime-Based Resource Management for Memory Allocation and BeyondCommunications of the ACM10.1145/361101867:4(87-96)Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1145/3611018
Jiang ZBai JLu KHu SCapkun SRoesner F(2020)Fuzzing error handling code using context-sensitive software fault injectionProceedings of the 29th USENIX Conference on Security Symposium10.5555/3489212.3489358(2595-2612)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.5555/3489212.3489358
Maas MAndersen DIsard MJavanmard MMcKinley KRaffel CLarus JCeze LStrauss K(2020)Learning-based Memory Allocation for C++ Server WorkloadsProceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3373376.3378525(541-556)Online publication date: 9-Mar-2020
https://dl.acm.org/doi/10.1145/3373376.3378525
Zeng QKayas GMohammed ELuo LDu XRhee J(2019)HeapTherapy+: Efficient Handling of (Almost) All Heap Vulnerabilities Using Targeted Calling-Context Encoding2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)10.1109/DSN.2019.00060(530-542)Online publication date: Jun-2019
https://doi.org/10.1109/DSN.2019.00060
Wu RXiao XCheung SZhang HZhang C(2016)Casper: an efficient approach to call trace collectionACM SIGPLAN Notices10.1145/2914770.283761951:1(678-690)Online publication date: 11-Jan-2016
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Wu RXiao XCheung SZhang HZhang CBodik RMajumdar R(2016)Casper: an efficient approach to call trace collectionProceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages10.1145/2837614.2837619(678-690)Online publication date: 11-Jan-2016
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Servat HLlort GGonzalez JGimssnez JLabarta J(2016)Bio-Inspired Call-Stack Reconstruction for Performance Analysis2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)10.1109/PDP.2016.62(82-90)Online publication date: Feb-2016
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Wang LLi BLeung H(2016)A new method to encode calling contexts with recursions一种针对递归的调用语境编码技术Science China Information Sciences10.1007/s11432-015-5418-359:5Online publication date: 12-Apr-2016
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