SC15 Austin, TX

Accelerating Tridiagonal Matrix Inversion on the GPU

Authors: Bemnet Demere (Trinity College), Peter Yoon (Trinity College), Ebenezer Hormenou (Trinity College)

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.

Poster: pdf
Two-page extended abstract: pdf

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