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Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Accelerating Tridiagonal Matrix Inversion on the GPU

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

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

AUTHOR(S):Bemnet Demere, Peter Yoon, Ebenezer Hormenou

ROOM:Level 4 - Lobby

ABSTRACT:

Inverting a matrix is a more computationally challenging process than solving a linear system. However, in fields such as structural engineering, dynamic systems, and cryptography, computing the inverse of a matrix is inevitable. In this poster, we present an accelerated procedure for computing the inverse of diagonally dominant tridiagonal matrices on the GPU. The algorithm is based on the recursive application of the Sherman-Morrison formula for tridiagonal matrices. The preliminary experimental results on Nvidia Tesla K20c GPUs show that our GPU implementation of the inversion procedure outperforms the conventional CPU-based implementations with a speedup of up to 24x.

Chair/Author Details:

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

Bemnet Demere - Trinity College

Peter Yoon - Trinity College

Ebenezer Hormenou - Trinity College

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