LiteRace: Effective Sampling for Lightweight Data-Race Detection
- Daniel Marino ,
- Madanlal Musuvathi ,
- Satish Narayanasamy ,
- Madan Musuvathi
Conference on Programming Language Design and Implementation (PLDI '09) |
Published by Association for Computing Machinery, Inc.
Data races are one of the most common and subtle causes of pernicious concurrency bugs. Static techniques for preventing data races are overly conservative and do not scale well to large programs. Past research has produced several dynamic data race detectors that can be applied to large programs. They are precise in the sense that they only report actual data races. However, dynamic data race detectors incur a high performance overhead, slowing down a program’s execution by an order of magnitude. In this paper we present LiteRace, a very lightweight data race detector that samples and analyzes only selected portions of a program’s execution. We show that it is possible to sample a multithreaded program at a low frequency, and yet, find infrequently occurring data races. We implemented LiteRace using Microsoft’s Phoenix compiler. Our experiments with several Microsoft programs, Apache, and Firefox show that LiteRace is able to find more than 70% of data races by sampling less than 2% of memory accesses in a given program execution.
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all 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, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. The definitive version of this paper can be found at ACM's Digital Library --http://www.acm.org/dl/.