GPU Acceleration for Hermitian Eigensystems

作者: Michael T. Garba , Horacio González–Vélez , Daniel L. Roach

DOI: 10.1007/978-3-642-38496-7_10

关键词:

摘要: As a recurrent problem in numerical analysis and computational science, eigenvector eigenvalue determination usually employs high-performance linear algebra libraries. This paper explores the implementation of routines for solution multiple large Hermitian systems on Graphics Processing Unit (GPU). We report performance increase up to two orders magnitude over original \textscEispack with NVIDIA Tesla C2050 GPU, providing an effective order unit cell size or simulated resolution Inelastic Neutron Scattering (INS) modelling from atomistic simulations.

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