Fast weighting method for plasma PIC simulation on GPU-accelerated heterogeneous systems
来源期刊：中南大学学报(英文版)2013年第6期
论文作者：YANG Can-qun(杨灿群) WU Qiang(吴强) HU Hui-li(胡慧俐) SHI Zhi-cai(石志才) CHEN Juan(陈娟) TANG Tao(唐滔)
文章页码：1527 - 1535
Key words：GPU computing; heterogeneous computing; plasma physics simulations; particle-in-cell (PIC)
Abstract: Particle-in-cell (PIC) method has got much benefits from GPU-accelerated heterogeneous systems. However, the performance of PIC is constrained by the interpolation operations in the weighting process on GPU (graphic processing unit). Aiming at this problem, a fast weighting method for PIC simulation on GPU-accelerated systems was proposed to avoid the atomic memory operations during the weighting process. The method was implemented by taking advantage of GPU’s thread synchronization mechanism and dividing the problem space properly. Moreover, software managed shared memory on the GPU was employed to buffer the intermediate data. The experimental results show that the method achieves speedups up to 3.5 times compared to previous works, and runs 20.08 times faster on one NVIDIA Tesla M2090 GPU compared to a single core of Intel Xeon X5670 CPU.
YANG Can-qun(杨灿群), WU Qiang(吴强), HU Hui-li(胡慧俐), SHI Zhi-cai(石志才), CHEN Juan(陈娟), TANG Tao(唐滔)
(School of Computer Science, National University of Defense Technology, Changsha 410073, China)
Abstract:Particle-in-cell (PIC) method has got much benefits from GPU-accelerated heterogeneous systems. However, the performance of PIC is constrained by the interpolation operations in the weighting process on GPU (graphic processing unit). Aiming at this problem, a fast weighting method for PIC simulation on GPU-accelerated systems was proposed to avoid the atomic memory operations during the weighting process. The method was implemented by taking advantage of GPU’s thread synchronization mechanism and dividing the problem space properly. Moreover, software managed shared memory on the GPU was employed to buffer the intermediate data. The experimental results show that the method achieves speedups up to 3.5 times compared to previous works, and runs 20.08 times faster on one NVIDIA Tesla M2090 GPU compared to a single core of Intel Xeon X5670 CPU.
Key words:GPU computing; heterogeneous computing; plasma physics simulations; particle-in-cell (PIC)
