SC12 Home > SC12 Schedule > SC12 Presentation - Parametric Flows - Automated Behavior Equivalencing for Symbolic Analysis of Races in CUDA Programs

SCHEDULE: NOV 10-16, 2012

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Parametric Flows - Automated Behavior Equivalencing for Symbolic Analysis of Races in CUDA Programs

SESSION: Auto-Diagnosis of Correctness and Performance Issues

EVENT TYPE: Papers

TIME: 3:30PM - 4:00PM

SESSION CHAIR: Kenjiro Taura

AUTHOR(S):Peng Li, Guodong Li, Ganesh Gopalakrishnan

ROOM:255-BC

ABSTRACT:
The growing scale of concurrency requires automated abstraction techniques to cut down the effort in concurrent system analysis. In this paper, we show that the high degree of behavioral symmetry present in GPU programs allows CUDA race detection to be dramatically simplified through abstraction. Our abstraction techniques is one of automatically creating parametric flows---control-flow equivalence classes of threads that diverge in the same manner---and checking for data races only across a pair of threads per parametric flow. We have implemented this approach as an extension of our recently proposed GKLEE symbolic analysis framework and show that all our previous results are dramatically improved in that (i) the parametric flow-based analysis takes far less time, and (ii) because of the much higher scalability of the analysis, we can detect even more data race situations that were previously missed by GKLEE because it was forced to downscale examples to limit analysis complexity.

Chair/Author Details:

Kenjiro Taura (Chair) - University of Tokyo

Peng Li - University of Utah

Guodong Li - Fujitsu Laboratories of America

Ganesh Gopalakrishnan - University of Utah

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Parametric Flows - Automated Behavior Equivalencing for Symbolic Analysis of Races in CUDA Programs

SESSION: Auto-Diagnosis of Correctness and Performance Issues

EVENT TYPE:

TIME: 3:30PM - 4:00PM

SESSION CHAIR: Kenjiro Taura

AUTHOR(S):Peng Li, Guodong Li, Ganesh Gopalakrishnan

ROOM:255-BC

ABSTRACT:
The growing scale of concurrency requires automated abstraction techniques to cut down the effort in concurrent system analysis. In this paper, we show that the high degree of behavioral symmetry present in GPU programs allows CUDA race detection to be dramatically simplified through abstraction. Our abstraction techniques is one of automatically creating parametric flows---control-flow equivalence classes of threads that diverge in the same manner---and checking for data races only across a pair of threads per parametric flow. We have implemented this approach as an extension of our recently proposed GKLEE symbolic analysis framework and show that all our previous results are dramatically improved in that (i) the parametric flow-based analysis takes far less time, and (ii) because of the much higher scalability of the analysis, we can detect even more data race situations that were previously missed by GKLEE because it was forced to downscale examples to limit analysis complexity.

Chair/Author Details:

Kenjiro Taura (Chair) - University of Tokyo

Peng Li - University of Utah

Guodong Li - Fujitsu Laboratories of America

Ganesh Gopalakrishnan - University of Utah

Add to iCal  Click here to download .ics calendar file

Add to Outlook  Click here to download .vcs calendar file

Add to Google Calendarss  Click here to add event to your Google Calendar