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Published in:
2013 | OriginalPaper | Chapter
GPU Acceleration for Hermitian Eigensystems
Authors : Michael T. Garba, Horacio González–Vélez, Daniel L. Roach
Published in: Transactions on Computational Collective Intelligence X
Publisher: Springer Berlin Heidelberg
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As a recurrent problem in numerical analysis and computational science, eigenvector and eigenvalue determination usually employs high-performance linear algebra libraries. This paper explores the implementation of high-performance routines for the solution of multiple large Hermitian eigenvector and eigenvalue systems on a Graphics Processing Unit (GPU). We report a performance increase of up to two orders of magnitude over the original
$\textsc{Eispack} {}$
routines with a NVIDIA Tesla C2050 GPU, providing an effective order of magnitude increase in unit cell size or simulated resolution for Inelastic Neutron Scattering (INS) modelling from atomistic simulations.