Experimental Performance Comparison of Dynamic Data Race Detection Techniques

Data races are one of the most difficult types of bugs in concurrent multithreaded systems. It requires significant time and cost to accurately detect bugs in complex large‐scale programs. Although many race detection techniques have been proposed by various researchers, none of them are effective i...

Full description

Saved in:
Bibliographic Details
Published in:ETRI journal Vol. 39; no. 1; pp. 124 - 134
Main Authors: Yu, Misun, Park, Seung‐Min, Chun, Ingeol, Bae, Doo‐Hwan
Format: Journal Article
Language:English
Published: Electronics and Telecommunications Research Institute (ETRI) 01.02.2017
한국전자통신연구원
Subjects:
Causally precedes
Data race
Debugging
Dynamic detection
happens before
Lockset
Multithreaded programming
전자/정보통신공학
ISSN:1225-6463, 2233-7326
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Data races are one of the most difficult types of bugs in concurrent multithreaded systems. It requires significant time and cost to accurately detect bugs in complex large‐scale programs. Although many race detection techniques have been proposed by various researchers, none of them are effective in all aspects. In this paper, we compare the performance of five recent dynamic race detection techniques: FastTrack, Acculock, Multilock‐HB, SimpleLock+, and causally precedes (CP) detection. We experimentally demonstrate the strengths and weaknesses of these dynamic race detection techniques in terms of their detection capability, running time, and runtime overhead using 20 benchmark programs with different characteristics. The comparison results show that the detection capability of CP detection does not differ from that of FastTrack, and that SimpleLock+ generates the lowest overhead among the hybrid detection techniques (Acculock, SimpleLock+, and Multilock‐HB) for all benchmark programs. SimpleLock+ is 1.2 times slower than FastTrack on average, but misses one true data race reported from Mutilock‐HB on the large‐scale benchmark programs.
Bibliography:Misun Yu (corresponding author
are with the SW & Content Research Laboratory, ETRI, Daejeon, Rep. of Korea.
bae@se.kaist.ac.kr
msyu@etri.re.kr
Seung‐Min Park
minpark@etri.re.kr
igchun@etri.re.kr
Doo‐Hwan Bae
This work was supported by Dual Use Technology Program through Civil Military Technology Cooperation Center funded by Ministry of Trade, Industry & Energy and Defense Acquisition Program Administration.
and Ingeol Chun
is with the Software engineering Laboratory, KAIST, Daejeon, Rep. of Korea.
https://etrij.etri.re.kr/etrij/journal/getPublishedPaperFile.do?fileId=SPF-1485829839570
G704-001110.2017.39.1.004
ISSN:1225-6463
2233-7326
DOI:10.4218/etrij.17.0115.1027