Parallel global optimization of expensive simulation models like nonlinear partial differential equations (PDEs) can speed up model calibration or project design decisions, but the impact of memory management on the efficiency of using parallel global optimization methods has not been previously studied. This paper quantifies cache memory limitations arising during parallel optimization of expensive PDE models. An efficient parallel optimization algorithm is applied to model calibration for two different, expensive real-world PDEs (i.e., hydrodynamic and water quality analysis for a 250-hectare lake). One of these two lake models takes 4.5 h per simulation in serial, but that PDE simulation time per simulation increases to 12 h with parallel optimization if default processor scheduling strategy is used on a modern nonuniform memory access multicore platform. We proposed a novel mixed affinity scheduling strategy for parallel simulation optimization that increases computational efficiency by as much as 20% over the default affinity strategy.

中文翻译：

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通过处理器亲和调度提高 PDE 模型的全局并行优化速度

昂贵的仿真模型（如非线性偏微分方程 (PDE)）的并行全局优化可以加快模型校准或项目设计决策，但内存管理对使用并行全局优化方法的效率的影响以前没有研究过。本文量化了昂贵的 PDE 模型并行优化过程中出现的缓存限制。一种有效的并行优化算法被应用于对两种不同的、昂贵的现实世界 PDE 的模型校准（即，对 250 公顷湖泊的水动力和水质分析）。这两个湖泊模型中的一个模型每次连续模拟需要 4.5 小时，但是，如果在现代非均匀内存访问多核平台上使用默认处理器调度策略，则每次模拟的 PDE 模拟时间会增加到 12 小时，并进行并行优化。我们提出了一种用于并行模拟优化的新型混合亲和性调度策略，与默认亲和性策略相比，计算效率提高了 20%。