论文标题
分布式粒子对流的扩散负荷平衡的性能评估
Performance Assessment of Diffusive Load Balancing for Distributed Particle Advection
论文作者
论文摘要
粒子对流是从矢量场提取积分曲线的方法。由于负载不平衡问题，粒子对流的有效并行化是一项具有挑战性的任务，其中分配了过程不平等的工作负载，导致其中一些人在执行计算时会闲置。存在各种负荷平衡方法，但它们都涉及权衡取舍，例如加工间沟通或中央控制结构的需求。在这项工作中，我们根据扩散载荷平衡的家族提供了两种局部负载平衡方法，用于粒子对流。每个过程都可以访问其相邻过程的块，该过程可以基于邻里工作量定义的度量来动态共享粒子。通过强尺度和较弱的缩放以及负载失衡来评估这些方法。我们表明，这些方法降低了对流的总运行时间，并且在缩放范围内在孤立的过程邻里进行本地运行时有希望。
Particle advection is the approach for extraction of integral curves from vector fields. Efficient parallelization of particle advection is a challenging task due to the problem of load imbalance, in which processes are assigned unequal workloads, causing some of them to idle as the others are performing compute. Various approaches to load balancing exist, yet they all involve trade-offs such as increased inter-process communication, or the need for central control structures. In this work, we present two local load balancing methods for particle advection based on the family of diffusive load balancing. Each process has access to the blocks of its neighboring processes, which enables dynamic sharing of the particles based on a metric defined by the workload of the neighborhood. The approaches are assessed in terms of strong and weak scaling as well as load imbalance. We show that the methods reduce the total run-time of advection and are promising with regard to scaling as they operate locally on isolated process neighborhoods.
