LiteRace: Effective Sampling for Lightweight Data-Race Detection

Daniel Marino, Madanlal Musuvathi, and Satish Narayanasamy

Abstract

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.

Details

Publication typeProceedings
Published inConference on Programming Language Design and Implementation (PLDI '09)
PublisherAssociation for Computing Machinery, Inc.
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