article

Fast data-locality profiling of native execution

Authors:

Erik HagerstenAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 33, Issue 1

Pages 169 - 180

https://doi.org/10.1145/1071690.1064232

Published: 06 June 2005 Publication History

Abstract

Performance tools based on hardware counters can efficiently profile the cache behavior of an application and help software developers improve its cache utilization. Simulator-based tools can potentially provide more insights and flexibility and model many different cache configurations, but have the drawback of large run-time overhead.We present StatCache, a performance tool based on a statistical cache model. It has a small run-time overhead while providing much of the flexibility of simulator-based tools. A monitor process running in the background collects sparse memory access statistics about the analyzed application running natively on a host computer. Generic locality information is derived and presented in a code-centric and/or data-centric view.We evaluate the accuracy and performance of the tool using ten SPEC CPU2000 benchmarks. We also exemplify how the flexibility of the tool can be used to better understand the characteristics of cache-related performance problems.

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Fast data-locality profiling of native execution

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 33, Issue 1

Performance evaluation review

June 2005

417 pages

ISSN:0163-5999

DOI:10.1145/1071690

Issue’s Table of Contents

SIGMETRICS '05: Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
June 2005
428 pages
ISBN:1595930221
DOI:10.1145/1064212
General Chairs:
Derek Eager
University of Saskatchewan, Canada
,
Carey Williamson
University of Calgary, Canada
,
Program Chairs:
Sem Borst
Bell Labs, USA & CWI, The Netherlands
,
John C. S. Lui
Chinese University of Hong Kong, China

Copyright © 2005 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: 06 June 2005

Published in SIGMETRICS Volume 33, Issue 1

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Li BZhao QJiao SLiu XCosta XHassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)DroidPerf: Profiling Memory Objects on Android DevicesProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592503(1-15)Online publication date: 2-Oct-2023
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Sasongko MChabbi MKelly PUnat D(2023)Precise event sampling‐based data locality tools for AMD multicore architecturesConcurrency and Computation: Practice and Experience10.1002/cpe.770735:24Online publication date: 3-Apr-2023
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