利用OpenACC加速粒子-网格代码ECsim
Accelerating the Particle-In-Cell code ECsim with OpenACC
AI总结：
本文通过OpenACC加速ECsim代码，实现ExaScale架构下的高效能计算，展示出在Leonardo系统上5倍速度提升和3倍能耗降低的成果。
摘要：
粒子-网格（PIC）方法是一种广泛用于等离子体物理中模拟等离子体动能水平的计算技术。在本文中，我们致力于将半隐式能量守恒的PIC代码ECsim适应于ExaScale架构。为此，我们采用了基于 pragma 的加速策略，利用OpenACC 实现高性能的同时要求最小的代码重构。在预ExaScale的Leonardo系统上，加速代码相比CPU参考代码实现了5倍的速度提升和3倍的能耗降低。在多个NVIDIA GPU世代上的性能比较显示，GH200统一内存架构带来了显著优势。最后，对Leonardo系统上的强标度和弱标度测试显示，效率分别达到64和1024个GPU时的70%和78%。
英文摘要：
The Particle-In-Cell (PIC) method is a computational technique widely used in plasma physics to model plasmas at the kinetic level. In this work, we present our effort to prepare the semi-implicit energy-conserving PIC code ECsim for exascale architectures. To achieve this, we adopted a pragma-based acceleration strategy using OpenACC, which enables high performance while requiring minimal code restructuring. On the pre-exascale Leonardo system, the accelerated code achieves a $5 \times$ speedup and a $3 \times$ reduction in energy consumption compared to the CPU reference code. Performance comparisons across multiple NVIDIA GPU generations show substantial benefits from the GH200 unified memory architecture. Finally, strong and weak scaling tests on Leonardo demonstrate efficiency of $70 \%$ and $78 \%$ up to 64 and 1024 GPUs, respectively.
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