Method for quickening solving of mesoscopic scale physical problem by using graphic processing unit (GPU)

Abstract

The invention provides a method for quickening solving of a mesoscopic scale physical problem by using a graphic processing unit (GPU). The method fully uses characteristics of a many-core processor such as superstrong floating-point calculation capacity and large memory bandwidth owned by the GPU, Hamiltonian matrix initialization calculating work occupying the longest machine hours in mesoscopic scale problem solving is processed by the GPU in assisted mode, traditional matrix diagonalization work is still finished by a central processing unit (CPU) good in serial calculating capacity, and quickening of the mesoscopic scale physical problem solving is achieved. In a calculating process, users use characteristics that Hamiltonian matrix element calculation is independent relatively, simple mapping from a problem space to a GPU thread space is achieved, and therefore usability and high expandability of an algorithm are guaranteed.

Description

technical field [0001] The present invention relates to the application of computational physics and nano-material science in the field of high-performance computing, and specifically relates to a method of using GPU to accelerate large-scale Hamiltonian matrix initialization calculations in the solution of mesoscopic system physics problems to solve micro, Nanoelectronic Structure Methods. Specifically, it is a method of using GPU to accelerate the solution of quantum mesoscopic system. Background technique [0002] With the continuous progress of human society and the development of science and technology, people's understanding of nature has become more and more extensive, and gradually deepened into physical systems such as micro and nano. At the same time, the needs of biotechnology, chemical production and pharmaceuticals also urgently require people to accurately grasp the properties of objects at the electronic, atomic or molecular scale, so the study of mesoscopic ...

AI Technical Summary

Problems solved by technology

In order to make the calculation results as accurate as possible or to meet the needs of the calculation system, a large number of base vectors (set as M) are often required in practice, but as the number of base vectors increases, the scale of the Hamiltonian matrix also expands sharply (M 2 ), which leads to heavy initialization of the Hamiltonian matrix, and even most of the calculation time will be spent on the initial calculation of the Hamiltonian matrix elements

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