适用于异构集群的混合并行流线生成系统

流线是流场可视化的主要方法之一,而针对大规模流场的流线生成由于计算量大往往需要采用高性能计算机这样的并行计算环境结合并行化算法以实现计算加速.在当前异构计算系统越来越普遍的情况下,为了充分利用并行异构计算环境的计算能力,实现更高效的并行流线生成,本文采用了基于数据并行原语结合分布式消息通讯的技术架构,设计了一套适用于异构集群的混合并行流线生成系统,并在此基础上针对数据分块、数据冗余化及进程通讯策略等方面进行设计,提出并实现了一套并行粒子追踪算法.该系统被部署于国产超算平台上,并针对大规模CFD流场模拟结果数据可视化应用开展了实验.本文给出了相关实验结果,分析了核心并行算法的速度性能、可扩展性以及负载均衡等方面情况,说明了系统及算法的有效性和可扩展性.

Hybrid Parallel Streamline Generation System Suitable for Heterogeneous Clusters

Streamline is one of the main methods of flow visualization. In light of a large amount of computation, the streamline generation from large flow fields usually requires parallel computing environments, such as high-performance computers and parallel algorithms, to accelerate computation. As wider application of heterogeneous computing systems, we design a hybrid parallel streamline generation system suitable for heterogeneous clusters in terms of data decomposition, overlapping and communication strategy with technologies such as data-parallel primitives and message passing interface to maximize the computing power of the heterogeneous parallel computing environment and achieve more efficient parallel streamline generation. A set of algorithms related to parallel particle advection are proposed and implemented. The system is deployed on a domestic supercomputer, and experiments are conducted to visualize the results of a large-scale CFD flow field simulation. This study provides relevant experimental results and analyzes the performance, scalability, and load balance of the core parallel algorithm, verifying the effectiveness and scalability of the system and algorithm.