sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Practical Floating-Point Divergence Detection

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, ACM Student Research Competition Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Wei-Fan Chiang

ROOM:Level 4 - Lobby


Reducing floating-point precision allocation in HPC programs is of considerable interest from the point of view of obtaining higher performance.
However, this can lead to unexpected behavioral deviations from the programmer's intent.
In this paper, we focus on the problem of divergence detection: when a given floating-point program exhibits different control flow (or differs in terms of other discrete outputs) with respect to the same program interpreted under reals.
This problem has remained open even for everyday programs such as those that compute convex-hulls.
We propose a classification of the divergent behaviors exhibited by programs, and propose efficient heuristics to generate inputs causing divergence.
Our experimental results demonstrate that our input generation heuristics are far more efficient than random input generation for divergence detection, and can exhibit divergence even for programs with thousands of inputs.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Wei-Fan Chiang - University of Utah

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