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2015 International Conference on Parallel Architecture and Compilation (PACT) (2015)
San Francisco, CA, USA
Oct. 18, 2015 to Oct. 21, 2015
ISSN: 1089-795X
ISBN: 978-1-4673-9524-3
pp: 266-279
Dataflow analysis-based dynamic parallel monitoring(DADPM) is a recent approach for identifying bugsin parallel software as it executes, based on the key insightof explicitly modeling a sliding window of uncertainty acrossparallel threads. While this makes the approach practical andscalable, it also introduces the possibility of false positives inthe analysis. In this paper, we improve upon the DADPMframework through two observations. First, by explicitlytracking new "uncertain" states in the metadata lattice, wecan distinguish potential false positives from true positives. Second, as the analysis tool runs dynamically, it can use theexistence (or absence) of observed uncertain states to adjustthe tradeoff between precision and performance on-the-fly. Forexample, we demonstrate how the epoch size parameter canbe adjusted dynamically in response to uncertainty in orderto achieve better performance and precision than when thetool is statically configured. This paper shows how to adapt acanonical dataflow analysis problem (reaching definitions) anda popular security monitoring tool (TAINTCHECK) to our newuncertainty-tracking framework, and provides new provableguarantees that reported true errors are now precise.
Uncertainty, Instruction sets, Monitoring, Metadata, Analytical models, Security
Michelle L. Goodstein, Phillip B. Gibbons, Michael A. Kozuch, Todd C. Mowry, "Tracking and Reducing Uncertainty in Dataflow Analysis-Based Dynamic Parallel Monitoring", 2015 International Conference on Parallel Architecture and Compilation (PACT), vol. 00, no. , pp. 266-279, 2015, doi:10.1109/PACT.2015.20
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