research-article

Algorithmic profiling

Authors:

Dmitrijs Zaparanuks and

Matthias HauswirthAuthors Info & Claims

PLDI '12: Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2012

Pages 67 - 76

https://doi.org/10.1145/2254064.2254074

Published: 11 June 2012 Publication History

Abstract

Traditional profilers identify where a program spends most of its resources. They do not provide information about why the program spends those resources or about how resource consumption would change for different program inputs. In this paper we introduce the idea of algorithmic profiling. While a traditional profiler determines a set of measured cost values, an algorithmic profiler determines a cost function. It does that by automatically determining the "inputs" of a program, by measuring the program's "cost" for any given input, and by inferring an empirical cost function.

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Pham LSaad FHoffmann J(2024)Robust Resource Bounds with Static Analysis and Bayesian InferenceProceedings of the ACM on Programming Languages10.1145/36563808:PLDI(76-101)Online publication date: 20-Jun-2024
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Zhao GGeorgiou SHassan SZou YTruong DCorbin TSpinellis DConstantinou EBacchelli A(2024)Enhancing Performance Bug Prediction Using Performance Code MetricsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644920(50-62)Online publication date: 15-Apr-2024
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Index Terms

Algorithmic profiling

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Information & Contributors

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Published In

cover image ACM Conferences

PLDI '12: Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2012

572 pages

ISBN:9781450312059

DOI:10.1145/2254064

General Chairs:
Jan Vitek
Purdue University
,
Haibo Lin
Microsoft China
,
Program Chair:
Frank Tip
IBM T.J. Watson Research Center

ACM SIGPLAN Notices Volume 47, Issue 6
PLDI '12
June 2012
534 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2345156
Issue’s Table of Contents

Copyright © 2012 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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New York, NY, United States

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Published: 11 June 2012

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PLDI '12

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PLDI '12: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 11 - 16, 2012

Beijing, China

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PLDI '12 Paper Acceptance Rate 48 of 255 submissions, 19%;

Overall Acceptance Rate 406 of 2,067 submissions, 20%

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Cited By

Pham LSaad FHoffmann J(2024)Robust Resource Bounds with Static Analysis and Bayesian InferenceProceedings of the ACM on Programming Languages10.1145/36563808:PLDI(76-101)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656380
Zhao GGeorgiou SHassan SZou YTruong DCorbin TSpinellis DConstantinou EBacchelli A(2024)Enhancing Performance Bug Prediction Using Performance Code MetricsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644920(50-62)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644920
Alsaeed ZYoung MJust RFraser G(2023)Finding Short Slow Inputs Faster with Grammar-Based SearchProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598118(1068-1079)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3597926.3598118
Li JZhang YLu SGunawi HGu XHuang FLi D(2023)Performance Bug Analysis and Detection for Distributed Storage and Computing SystemsACM Transactions on Storage10.1145/358028119:3(1-33)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3580281
Qin BTu TLiu ZYu TSong L(2022)Algorithmic Profiling for Real-World Complexity ProblemsIEEE Transactions on Software Engineering10.1109/TSE.2021.306765248:7(2680-2694)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSE.2021.3067652
Ishimwe DNguyen KNguyen T(2021)Dynaplex: analyzing program complexity using dynamically inferred recurrence relationsProceedings of the ACM on Programming Languages10.1145/34855155:OOPSLA(1-23)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485515
Gottschall BEeckhout LJahre M(2021)TIP: Time-Proportional Instruction ProfilingMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480058(15-27)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3466752.3480058
Rogora DCarzaniga ADiwan AHauswirth MSoulé RBilas AMagoutis KMarkatos EKostic DSeltzer M(2020)Analyzing system performance with probabilistic performance annotationsProceedings of the Fifteenth European Conference on Computer Systems10.1145/3342195.3387554(1-14)Online publication date: 15-Apr-2020
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He HJia ZLi SXu EYu TYu YWang JLiao XGrundy JLe Goues CLo D(2020)CP-detectorProceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering10.1145/3324884.3416531(623-634)Online publication date: 21-Dec-2020
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Incerto ENapolitano ATribastone M(2020)Statistical Learning of Markov Chains of Programs2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS50786.2020.9285947(1-8)Online publication date: 17-Nov-2020
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