SPUMA：一种面向OPENFOAM GPU移植的微创方法 (SPUMA: a minimally invasive approach to the GPU porting of OPENFOAM)

High Performance Computing (HPC) on hybrid clusters represents a significant opportunity for Computational Fluid Dynamics (CFD), especially when modern accelerators are utilized effectively. However, despite the widespread adoption of GPUs, programmability remains a challenge, particularly in open-source contexts. In this paper, we present SPUMA, a full GPU porting of OPENFOAM targeting NVIDIA and AMD GPUs. The implementation strategy is based on a portable programming model and the adoption of a memory pool manager that leverages the unified memory feature of modern GPUs. This approach is discussed alongside several numerical tests conducted on two pre-exascale clusters in Europe, LUMI and Leonardo, which host AMD MI250X and NVIDIA A100 GPUs, respectively. In the performance analysis section, we present results related to memory usage profiling and kernel wall-time, the impact of the memory pool, and energy consumption obtained by simulating the well-known DrivAer industrial test case. GPU utilization strongly affects strong scalability results, reaching 65% efficiency on both LUMI and Leonardo when approaching a load of 8 million cells per GPU. Weak scalability results, obtained on 20 GPUs with the OpenFOAM native multigrid solver, range from 75% on Leonardo to 85% on LUMI. Notably, efficiency is no lower than 90% when switching to the NVIDIA AmgX linear algebra solver. Our tests also reveal that one A100 GPU on Leonardo is equivalent 200-300 Intel Sapphire Rapids cores, provided the GPUs are sufficiently oversubscribed (more than 10 million of cells per GPU). Finally, energy consumption is reduced by up to 82% compared to analogous simulations executed on CPUs.

翻译：混合集群上的高性能计算（HPC）为计算流体力学（CFD）带来了重要机遇，尤其是在现代加速器得到有效利用的情况下。然而，尽管GPU已被广泛采用，其可编程性仍然是一个挑战，在开源环境中尤为突出。本文提出了SPUMA，一个面向NVIDIA和AMD GPU的完整OPENFOAM GPU移植方案。该实现策略基于可移植编程模型，并采用了利用现代GPU统一内存特性的内存池管理器。文中结合在欧洲两个准百亿亿次级集群LUMI和Leonardo上进行的多项数值测试对此方法进行了讨论，这两个集群分别搭载了AMD MI250X和NVIDIA A100 GPU。在性能分析部分，我们展示了通过模拟著名的DrivAer工业测试案例所获得的内存使用剖析与内核运行时间结果、内存池的影响以及能耗数据。GPU利用率对强可扩展性结果有显著影响，在每GPU负载接近800万个网格单元时，LUMI和Leonardo上的效率均达到65%。使用OpenFOAM原生多重网格求解器在20个GPU上获得的弱可扩展性结果，在Leonardo上为75%，在LUMI上为85%。值得注意的是，当切换至NVIDIA AmgX线性代数求解器时，效率不低于90%。我们的测试还表明，在GPU负载充分超额分配（每GPU超过1000万个网格单元）的条件下，Leonardo上的一个A100 GPU相当于200-300个Intel Sapphire Rapids核心。最终，与在CPU上执行的同类模拟相比，能耗降低了高达82%。