Development and performance of a HemeLB GPU code for human-scale blood flow simulation

In recent years, it has become increasingly common for high performance computers (HPC) to possess some level of heterogeneous architecture - typically in the form of GPU accelerators. In some machines these are isolated within a dedicated partition, whilst in others they are integral to all compute nodes - often with multiple GPUs per node - and provide the majority of a machine's compute performance. In light of this trend, it is becoming essential that codes deployed on HPC are updated to execute on accelerator hardware. In this paper we introduce a GPU implementation of the 3D blood flow simulation code HemeLB that has been developed using CUDA C++. We demonstrate how taking advantage of NVIDIA GPU hardware can achieve significant performance improvements compared to the equivalent CPU only code on which it has been built whilst retaining the excellent strong scaling characteristics that have been repeatedly demonstrated by the CPU version. With HPC positioned on the brink of the exascale era, we use HemeLB as a motivation to provide a discussion on some of the challenges that many users will face when deploying their own applications on upcoming exascale machines.近年来，高性能计算机（High Performance Computer, HPC）搭载一定程度的异构架构已愈发普遍，这类架构通常以图形处理器（Graphics Processing Unit, GPU）加速器的形式呈现。部分HPC系统中，GPU加速器被隔离在专属分区内；而在另一部分系统中，它们则集成于所有计算节点——通常每个节点配备多块GPU——并贡献了系统绝大多数的计算性能。鉴于这一发展趋势，部署于HPC系统的代码亟需进行适配更新，以实现在加速器硬件上的高效运行。本研究提出了一款基于CUDA C++开发的3D血流模拟代码HemeLB的GPU实现版本。研究验证，相较于该代码原本依托的纯中央处理器（Central Processing Unit, CPU）版本，借助英伟达（NVIDIA）GPU硬件可实现显著的性能提升，同时保留了CPU版本已被反复验证的优异强可扩展性特性。当前HPC正处于迈向百亿亿次计算时代的临界点，本研究以HemeLB为案例，探讨了众多用户在将自有应用部署于未来百亿亿次计算系统时将面临的若干挑战。