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arXiv 2502.06296physics.flu-dyn
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一种用于高雷诺数湍流壁面流动极端规模计算的铅笔分布式有限差分求解器

A pencil-distributed finite-difference solver for extreme-scale calculations of turbulent wall flows at high Reynolds number

Rafael Diez Sanhueza, Jurriaan Peeters, Pedro Costa

AI总结:

本文提出一种铅笔分布式有限差分求解器,用于高雷诺数湍流壁面流动的极端规模模拟,通过改进泊松求解器和重设计的并行循环归约算法,在高达800亿网格点和1024块GPU上实现比基线快约两倍的性能,且无扩展性损失。

摘要:

我们提出了一种用于高雷诺数不可压缩湍流壁面流动极端规模模拟的计算方法。该数值算法扩展了一种流行的求解二阶有限差分泊松/亥姆霍兹方程的方法,采用铅笔分布式并行三对角求解器以提高大规模计算性能。我们研究了该方法在高雷诺数湍流槽道流动模拟中的优势,模拟规模高达约800亿个网格点,并在欧洲旗舰超级计算机Leonardo和LUMI上使用多达1024块GPU。对于后者,我们还需对整个求解器进行额外的GPU移植工作。我们的结果证实,虽然一维域分解在较小系统上具有优势,但在大规模下会变得低效甚至不可行。通过采用铅笔分布式方法,这一限制得以放宽。结果表明,在大规模下,改进后的泊松求解器比采用全转置算法的二维域分解基线方法快约两倍。强扩展性和弱扩展性测试表明,性能提升源于更低的通信开销。此外,为确保求解壁面法向隐式扩散时的高性能,我们提出了一种改进的并行循环归约(PCR)变体,将其分为预处理和运行时两个步骤。在预处理阶段,由并行GPU线程计算具有独立一维系数的小子数组,无需任何全局GPU通信。然后,在运行时,改进的PCR能够快速求解隐式一维扩散,且无计算开销。我们的结果表明,整个数值求解器与PCR算法相结合,能够实现采用二维铅笔分解的极端规模模拟,即使与适用于较小系统的最佳一维板条配置相比,也不会出现性能损失。

英文摘要:

We present a computational method for extreme-scale simulations of incompressible turbulent wall flows at high Reynolds numbers. The numerical algorithm extends a popular method for solving second-order finite differences Poisson/Helmholtz equations using a pencil-distributed parallel tridiagonal solver to improve computational performance at scale. The benefits of this approach were investigated for high-Reynolds-number turbulent channel flow simulations, with up to about 80 billion grid points and 1024 GPUs on the European flagship supercomputers Leonardo and LUMI. An additional GPU porting effort of the entire solver had to be undertaken for the latter. Our results confirm that, while 1D domain decompositions are favorable for smaller systems, they become inefficient or even impossible at large scales. This restriction is relaxed by adopting a pencil-distributed approach. The results show that, at scale, the revised Poisson solver is about twice as fast as the baseline approach with the full-transpose algorithm for 2D domain decompositions. Strong and weak scalability tests show that the performance gains are due to the lower communication footprint. Additionally, to secure high performance when solving for wall-normal implicit diffusion, we propose a reworked flavor of parallel cyclic reduction (PCR) that is split into pre-processing and runtime steps. During pre-processing, small sub-arrays with independent 1D coefficients are computed by parallel GPU threads, without any global GPU communication. Then, at runtime, the reworked PCR enables a fast solution of implicit 1D diffusion without computational overhead. Our results show that the entire numerical solver, coupled with the PCR algorithm, enables extreme-scale simulations with 2D pencil decompositions, which do not suffer performance losses even when compared to the best 1D slab configurations available for smaller systems.

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