Synergia CUDA: GPU-accelerated accelerator modeling package

Abstract

Synergia is a parallel, 3-dimensional space-charge particle-in-cell accelerator modeling code. We present our work porting the purely MPI-based version of the code to a hybrid of CPU and GPU computing kernels. The hybrid code uses the CUDA platform in the same framework as the pure MPI solution. We have implemented a lock-free collaborative charge-deposition algorithm for the GPU, as well as other optimizations, including local communication avoidance for GPUs, a customized FFT, and fine-tuned memory access patterns. On a small GPU cluster (up to 4 Tesla C1070 GPUs), our benchmarks exhibit both superior peak performance and better scaling than a CPU cluster with 16 nodes and 128 cores. We also compare the code performance on different GPU architectures, including C1070 Tesla and K20 Kepler.