Daniel Marino, Madanlal Musuvathi, and Satish Narayanasamy
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.
|Published in||Conference on Programming Language Design and Implementation (PLDI '09)|
|Publisher||Association for Computing Machinery, Inc.|
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 email@example.com. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.