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Portable GPU implementation of the WP-CCC ion-atom collisions code (2403.04252v1)

Published 7 Mar 2024 in physics.comp-ph and cs.DC

Abstract: We present our experience of porting the code used in the wave-packet convergent-close-coupling (WP-CCC) approach to run on NVIDIA V100 and AMD MI250X GPUs. The WP-CCC approach is a method used in the field of ion-atom collision physics to describe various processes such as elastic scattering, target excitation and electron-capture by the projectile. It has demonstrated its effectiveness in modelling collisions involving proton or bare ion projectiles with various atomic and molecular targets, especially those which can be considered as one or two-electron systems. Such calculations find their application in computational atomic physics as well as in the modelling of fusion plasmas and in hadron therapy for cancer treatment. The main computational cost of the method lies in the solution of an emerging set of coupled first-order differential equations. This involves implementing the standard Runge-Kutta method while varying the projectile position along multiple straight-line paths. At each projectile position several millions of matrix elements need to be calculated which is accomplished using the OpenACC programming model. Once these matrix elements are computed, the subsequent steps involve matrix inversion and multiplication with another matrix. To expedite these operations, a GPU-accelerated LAPACK routine, specialised for solving systems of linear equations, is employed. For AMD GPUs, this routine is accessible through the hipSOLVER library, while for NVIDIA GPUs, it can be obtained from the cuSOLVER library. The portability, performance and energy efficiency of the CPU-only code have been compared with the GPU-accelerated version running on AMD and NVIDIA GPUs. The implementation of GPU-accelerated WP-CCC code opens up avenues for exploring more sophisticated collision processes involving complex projectile and target structures, which were previously considered infeasible.

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