The Community for Technology Leaders
2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT) (2012)
Minneapolis, MN, USA
Sept. 19, 2012 to Sept. 23, 2012
ISBN: 978-1-5090-6609-4
pp: 201-212
Michelle L. Goodstein , Carnegie Mellon University, USA
Shimin Chen , HP Labs China, China
Phillip B. Gibbons , Intel Labs Pittsburgh, USA
Michael A. Kozuch , Intel Labs Pittsburgh, USA
Todd C. Mowry , Carnegie Mellon University, USA
Software lifeguards, or tools that monitor applications at runtime, are an effective way of identifying program errors and security exploits. Parallel programs are susceptible to a wider range of possible errors than sequential programs, making them even more in need of online monitoring. Unfortunately, monitoring parallel applications is difficult due to inter-thread data dependences. In prior work, we introduced a new software framework for online parallel program monitoring inspired by dataflow analysis, called Butterfly Analysis. Butterfly Analysis uses bounded windows of uncertainty to model the finite upper bound on delay between when an instruction is issued and when all its effects are visible throughout the system. While Butterfly Analysis offers many advantages, it ignored one key source of ordering information which affected its false positive rate: explicit software synchronization, and the corresponding high-level happens-before arcs. In this work we introduce Chrysalis Analysis, which extends the Butterfly Analysis framework to incorporate explicit happens-before arcs resulting from high-level synchronization within a monitored program. We show how to adapt two standard dataflow analysis techniques and two memory and security lifeguards to Chrysalis Analysis, using novel techniques for dealing with the many complexities introduced by happens-before arcs. Our security tool implementation shows that Chrysalis Analysis matches the key advantages of Butterfly Analysis—parallel monitoring, no detailed inter-thread data dependence tracking, no strong memory consistency requirements, and no missed errors—while significantly reducing the number of false positives.
Monitoring, Synchronization, Software, Metadata, Security, Uncertainty, Clocks,High-Level Synchronization, Data Flow Analysis, Parallel Programming, Dynamic Program Monitoring, Vector Clocks
Michelle L. Goodstein, Shimin Chen, Phillip B. Gibbons, Michael A. Kozuch, Todd C. Mowry, "Chrysalis analysis: Incorporating synchronization arcs in dataflow-analysis-based parallel monitoring", 2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT), vol. 00, no. , pp. 201-212, 2012, doi:
100 ms
(Ver 3.3 (11022016))