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Detecting Correlation Violations and Data Races by Inferring Non-deterministic Reads

Detecting Correlation Violations and Data Races by Inferring Non-deterministic Reads
Tagung:

Konferenzartikel 

Jahr:

2013 

Autoren:

Ali Jannesari
Nico Koprowski
Jochen Schimmel
Felix Wolf
Walter F. Tichy

Links:PDF

Summary

With the introduction of multicore systems and parallel programs concurrency bugs have become more common. A notorious class of these bugs are data races that violate correlations between variables. This happens, for example, when the programmer does not update correlated variables atomically, which is needed to maintain their semantic relationship. The detection of such races is challenging because correlations among variables usually escape traditional race detectors which are oblivious of semantic relationships. In this paper, we present an effective method for dynamically identifying correlated variables together with a race detector based on the notion of non-deterministic reads that identifies malicious data races on correlated variables. In eight programs and 190 micro benchmarks, we found more than 100 races that were overlooked by other race detectors. Furthermore, we identified about 300 variable correlations which were violated by these races.

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Bibtex

@inproceedings{tichy13f,
author={Ali Jannesari, Nico Koprowski, Jochen Schimmel, Felix Wolf, Walter F. Tichy},
title={Detecting Correlation Violations and Data Races by Inferring Non-deterministic Reads},
year=2013,
month=Dec.,
booktitle={Int’l Conference on Parallel and Distributed Systems (ICPADS)},
publisher={IEEE},
url={https://ps.ipd.kit.edu/downloads/},
doi={http://doi.ieeecomputersociety.org/10.1109/ICPADS.2013.14},
abstract={With the introduction of multicore systems and parallel programs concurrency bugs have become more common. A notorious class of these bugs are data races that violate correlations between variables. This happens, for example, when the programmer does not update correlated variables atomically, which is needed to maintain their semantic relationship. The detection of such races is challenging because correlations among variables usually escape traditional race detectors which are oblivious of semantic relationships. In this paper, we present an effective method for dynamically identifying correlated variables together with a race detector based on the notion of non-deterministic reads that identifies malicious data races on correlated variables. In eight programs and 190 micro benchmarks, we found more than 100 races that were overlooked by other race detectors. Furthermore, we identified about 300 variable correlations which were violated by these races.},
pages={1-9},