CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
research
CARISMA: a context-sensitive approach to race-condition sample-instance selection for multithreaded applications
Authors
WK Chan
TH Tse
B Xu
K Zhai
Publication date
1 January 2012
Publisher
'Association for Computing Machinery (ACM)'
Doi
Abstract
Dynamic race detectors can explore multiple thread schedules of a multithreaded program over the same input to detect data races. Although existing sampling-based precise race detectors reduce overheads effectively so that lightweight precise race detection can be performed in testing or post-deployment environments, they are ineffective in detecting races if the sampling rates are low. This paper presents CARISMA to address this problem. CARISMA exploits the insight that along an execution trace, a program may potentially handle many accesses to the memory locations created at the same site for similar purposes. Iterating over multiple execution trials of the same input, CARISMA estimates and distributes the sampling budgets among such location creation sites, and probabilistically collects a fraction of all accesses to the memory locations associated with such sites for subsequent race detection. Our experiment shows that, compared with PACER on the same platform and at the same sampling rate (such as 1%), CARISMA is significantly more effective. © 2012 ACM.postprin
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
HKU Scholars Hub
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:hub.hku.hk:10722/160101
Last time updated on 01/06/2016
CiteSeerX
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:CiteSeerX.psu:10.1.1.986.7...
Last time updated on 02/11/2017