GPU acceleration and performance of the particle-beam-dynamics code Elegant

Abstract

Elegant is an accelerator physics and particle-beam dynamics code widely used for modeling and design of a variety of high-energy particle accelerators and accelerator-based systems. In this paper we discuss a recently developed version of the code that can take advantage of CUDA-enabled graphics processing units (GPUs) to achieve significantly improved performance for a large class of simulations that are important in practice. The GPU version is largely defined by a framework that simplifies implementations of the fundamental kernel types that are used by Elegant: particle operations, reductions, particle loss, histograms, array convolutions and random number generation. Accelerated performance on the Titan Cray XK-7 supercomputer is approximately 6–10 times better with the GPU than all the CPU cores associated with the same node count. In addition to performance, the maintainability of the GPU-accelerated version of the code was considered a key design objective. Accuracy with respect to the CPU implementation is also a core consideration. Four different methods are used to ensure that the accelerated code faithfully reproduces the CPU results. Program summaryProgram Title: Kernels from the GPU-accelerated ElegantProgram Files doi:http://dx.doi.org/10.17632/jc465zy7p5.1Licensing provisions: MITProgramming language: C/C++/CUDANature of problem: The original design of the Elegant accelerator physics code was implemented on central processing units with message-passing interface parallelization. This implementation is not able to use next-generation multicore systems.Solution method: In this package we develop routines based on the CUDA language extensions to C++ that enable porting the Elegant code to be run on graphics processing units (GPUs). Special consideration is given to algorithms that require collective communication on the GPU.Additional comments including restrictions and unusual features: The full Elegant source code is freely available from Argonne National Laboratory and these distributions include the GPU code in the later releases.