PIC Skeleton Codes: GPU

GPU Tutorial

Tutorial for Computing on GPUs: GPUTutorial

GPU Codes with two levels of parallelism

These codes illustrate how to implement an optimal PIC algorithm on a GPU. It uses a hybrid tiling scheme with SIMD vectorization, both written with NVIDIA’s CUDA programming environment. The tiling algorithm used within a thread block on the GPU is the same as that used with OpenMP (Ref. [4]). Unlike the OpenMP implementation, however, each tile is controlled by a block of threads rather than a single thread, which requires a vectorized or data parallel implementation. Both CUDA C and CUDA Fortran interoperable versions are available, where a Fortran code can call the CUDA C libraries and a C code can call the CUDA Fortran libraries. For the 2D electrostatic code, a typical execution time for the particle part of this code on the M2090 GPU is about 0.9 ns/particle/time-step. For the 2-1/2D electromagnetic code, a typical execution time for the particle part of this code is about 2.0 ns/particle/time-step.

The first family uses CUDA C on the NVIDIA GPU, with a tiling technique for each thread block, and with SIMD vectorization within a block.

1. 2D Parallel Electrostatic Spectral code: gpupic2
2. 2-1/2D Parallel Electromagnetic Spectral code: gpubpic2

The second family uses CUDA Fortran on the NVIDIA GPU, with a tiling technique for each thread block, and with SIMD vectorization within a block.

1. 2D Parallel Electrostatic Spectral code: gpufpic2
2. 2-1/2D Parallel Electromagnetic Spectral code: gpufbpic2

GPU-MPI Codes with three levels of parallelism

These codes illustrate how to implement an optimal PIC algorithm on multiple GPUs. It uses a hybrid tiling scheme with SIMD vectorization on each GPU, and domain decomposition connecting such GPUs implemented with MPI. The algorithms used are described in Refs. [2-4]. Both CUDA C and CUDA Fortran interoperable versions are available, where a Fortran code can call the CUDA C libraries and a C code can call the CUDA Fortran libraries. For the 2D electrostatic code, a typical execution time for the particle part of this code on 108 M2090 GPUs is about 13 ps/particle/time-step. For the 2-1/2D electromagnetic code, a typical execution time for the particle part of this code is about 30 ps/particle/time-step. To put this into perspective, on 96 GPUs, a 2-1/2d electromagnetic simulation with 10 billion particles and a 16384x16384 grid takes about 0.5 sec/time step, including the field solver. The particle calculation is 3600 times faster than in the original serial code.

The first family uses CUDA C on the NVIDIA GPU, with a tiling technique for each thread block, SIMD vectorization within a block, and domain decomposition with MPI between GPUs.

1. 2D Parallel Electrostatic Spectral code: gpuppic2
2. 2-1/2D Parallel Electromagnetic Spectral code: gpupbpic2

The second family uses CUDA Fortran on the NVIDIA GPU, with a tiling technique for each thread block, SIMD vectorization within a block, and domain decomposition with MPI between GPUs.

1. 2D Parallel Electrostatic Spectral code: gpufppic2
2. 2-1/2D Parallel Electromagnetic Spectral code: gpufpbpic2

Want to contact the developer?

Send mail to Viktor Decyk – decyk@physics.ucla.edu