PPM - A highly efficient parallel particle-mesh library for the simulation of continuum systems
PPM - A highly efficient parallel particle-mesh library for the simulation of continuum systems
复制标题
DOI：
10.1016/j.jcp.2005.11.017
复制
发表时间：
2006-07-01
影响因子：
4.1
通讯作者：
Koumoutsakos, P.
中科院分区：
文献类型：
--
作者：
Sbalzarini, I. F.;Walther, J. H.;Koumoutsakos, P.
本文提出了一种高效的并行粒子网格（PPM）库，基于一个统一的粒子制定的连续系统的模拟。在这个配方中，网格的粒子方法的字符放松的网格的重新初始化的粒子，场方程的计算，和微分算子的离散化。目前利用的网格并没有减损的自适应性，有效地处理复杂的几何形状，最小的耗散，以及良好的稳定性能的粒子方法。网格和粒子的共存，允许开发一个一致的和自适应的数值方法，但它提出了一组具有挑战性的并行化问题，阻碍了在过去更广泛地使用粒子方法。本库解决了关键的并行化问题，涉及粒子网格插值和处理器粒子负载的平衡，使用一种新的自适应树混合域分解沿着与着色方案的粒子网格interpolation.The库的高并行效率在分布式存储器和共享内存向量架构上的一系列基准测试中证明。该方法的模块化显示了一系列的模拟，从可压缩的涡环使用一种新的配方的光滑粒子流体力学，在真实的生物细胞organelles.The本库中的扩散模拟，使不同的物理问题，使用自适应粒子方法的大规模模拟，并提供了一个计算工具，是一个可行的替代网格为基础的方法。(c)2005年爱思唯尔公司All rights reserved.
This paper presents a highly efficient parallel particle-mesh (PPM) library, based on a unifying particle formulation for the simulation of continuous systems. In this formulation, the grid-free character of particle methods is relaxed by the introduction of a mesh for the reinitialization of the particles, the computation of the field equations, and the discretization of differential operators. The present utilization of the mesh does not detract from the adaptivity, the efficient handling of complex geometries, the minimal dissipation, and the good stability properties of particle methods.The coexistence of meshes and particles, allows for the development of a consistent and adaptive numerical method, but it presents a set of challenging parallelization issues that have hindered in the past the broader use of particle methods. The present library solves the key parallelization issues involving particle-mesh interpolations and the balancing of processor particle loading, using a novel adaptive tree for mixed domain decompositions along with a coloring scheme for the particle-mesh interpolation.The high parallel efficiency of the library is demonstrated in a series of benchmark tests on distributed memory and on a shared-memory vector architecture. The modularity of the method is shown by a range of simulations, from compressible vortex rings using a novel formulation of smooth particle hydrodynamics, to simulations of diffusion in real biological cell organelles.The present library enables large scale simulations of diverse physical problems using adaptive particle methods and provides a computational tool that is a viable alternative to mesh-based methods. (c) 2005 Elsevier Inc. All rights reserved.