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SCHEDULE: NOV 15-20, 2015

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High Order Automatic Differentiation with MPI

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


ROOM:Level 4 - Lobby


Automatic Differentiation (AD) is an algorithmic technique for augmenting computer programs to compute derivatives accurately and efficiently. AD has two major modes: forward and reverse. Previous work on parallel reverse mode AD algorithms mainly focus on first order. In those work, the communication is considered as a remote value assignment. Then the communication is also reversed when evaluating derivatives.

Here, we describe a new parallel Reverse mode algorithm to compute higher order derivative for an MPI program. The innovation is that in a parallel reverse mode AD algorithm, a Send/Recv pair should not be considered only as a remote value assignment, but as dummy independent/dependent variables. The innovation permits communication to be treated analogous to computation. Hence communication does not need to be reversed, but can be re-performed in a separate phase. So complications created by reversing the communications is avoided.

Chair/Author Details:

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

Mu Wang - Purdue University

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