Accelerating the Particle-In-Cell code ECsim with OpenACC

TL;DR

This paper demonstrates how using OpenACC to accelerate the ECsim Particle-In-Cell code significantly improves performance and energy efficiency on exascale architectures, enabling scalable plasma simulations.

Contribution

The paper introduces a pragma-based OpenACC acceleration approach for ECsim, achieving high performance with minimal code changes for exascale computing.

Findings

5x speedup on Leonardo system

3x reduction in energy consumption

70-78% efficiency up to 1024 GPUs

Abstract

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.