High-Level Data Races

13 years 5 months ago

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Data races are a common problem in concurrent and multi-threaded programming. They are hard to detect without proper tool support. Despite the successful application of these tools, experience shows that the notion of data race is not powerful enough to capture certain types of inconsistencies occurring ice. In this paper we investigate data races on a higher abstraction layer. This enables us to detect inconsistent uses of shared variables, even if no classical race condition occurs. For example, a data structure representing a coordinate pair may have to be treated atomically. By lifting the meaning of a data race to a higher level, such problems can now be covered. The paper deﬁnes the concepts view and view consistency to give a notation for this novel kind of property. It describes what kinds of errors can be detected with this new deﬁnition, and where its limitations are. It also gives a formal guideline for using data structures in a multi-threading environment.

Cyrille Artho, Klaus Havelund, Armin Biere

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