OpenFPM：并行计算机上粒子和粒子网格编码的可扩展开放框架
OpenFPM: A scalable open framework for particle and particle-mesh codes on parallel computers
摘要：
可扩展和有效的数值模拟继续获得重要性，因为计算已被牢固地确立为发现的第三大支柱，与理论和实验并列。与此同时，计算硬件的性能通过增加异构并行性而增长，从而能够模拟越来越复杂的模型。然而，在异构的分布式硬件系统上有效地实现可扩展代码成为瓶颈。这个瓶颈可以通过中间软件层来缓解，这些软件层提供更接近问题域的更高级别的抽象，从而使计算科学家能够专注于仿真。在这里，我们提出OpenFPM，一个开放的和可扩展的框架，提供了一个抽象层的数值模拟使用粒子和/或网格。OpenFPM为离散和连续模型的纯粒子和混合粒子网格模拟以及非模拟代码的共享内存和分布式内存实现提供透明和可扩展的基础设施。该基础设施通过常用数值例程的可移植实现以及第三方库的接口来补充。我们提出的架构和设计的OpenFPM，详细的底层抽象，并基准框架的应用范围从光滑粒子流体动力学（SPH）的分子动力学（MD），离散元方法（DEM），涡方法，模板代码，高维蒙特卡罗采样（CMA-ES），和反应扩散求解器，比较它的当前状态的艺术和现有的软件框架。
英文摘要：
Scalable and efficient numerical simulations continue to gain importance, as computation is firmly established as the third pillar of discovery, alongside theory and experiment. Meanwhile, the performance of computing hardware grows through increasing heterogeneous parallelism, enabling simulations of ever more complex models. However, efficiently implementing scalable codes on heterogeneous, distributed hardware systems becomes the bottleneck. This bottleneck can be alleviated by intermediate software layers that provide higher-level abstractions closer to the problem domain, hence allowing the computational scientist to focus on the simulation. Here, we present OpenFPM, an open and scalable framework that provides an abstraction layer for numerical simulations using particles and/or meshes. OpenFPM provides transparent and scalable infrastructure for shared-memory and distributed-memory implementations of particles-only and hybrid particle-mesh simulations of both discrete and continuous models, as well as non-simulation codes. This infrastructure is complemented with portable implementations of frequently used numerical routines, as well as interfaces to third-party libraries. We present the architecture and design of OpenFPM, detail the underlying abstractions, and benchmark the framework in applications ranging from Smoothed-Particle Hydrodynamics (SPH) to Molecular Dynamics (MD), Discrete Element Methods (DEM), Vortex Methods, stencil codes, high-dimensional Monte Carlo sampling (CMA-ES), and Reaction-Diffusion solvers, comparing it to the current state of the art and existing software frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
