基于数据并行化的粒子平流：参与度、乒乓粒子与开销
Parallelize Over Data Particle Advection: Participation, Ping Pong Particles, and Overhead
AI总结：
本文深入分析了分布式内存环境下POD粒子平流算法性能不佳的原因，通过引入新指标和以粒子为中心的分析，发现进程间粒子移动的开销是影响整体执行时间的关键因素。
摘要：
粒子平流是可视化和分析的基础算法之一，对于理解科学模拟中常见的矢量场至关重要。在分布式内存环境中处理大规模数据时，实现高效性能是出了名的困难。由于其简单性且能最小化大型矢量场数据的移动，Parallelize over Data (POD) 算法已成为事实上的标准。尽管其简单且被广泛使用，但 POD 算法的扩展性问题已为人所知，并在文献中有所描述。在本文中，我们对 POD 算法进行了一系列深入分析，揭示了该算法性能不佳的根本原因。我们设计了一系列具有代表性的工作负载来研究 POD 算法的性能，并在超级计算机上执行它们，同时收集计时和统计数据以供分析。随后，我们进行了两种不同类型的分析。在第一种分析中，我们引入了两个新的指标，用于测量工作负载运行过程中的算法效率。第二种分析是从被平流粒子的角度进行的。使用以粒子为中心的分析，我们发现与进程间粒子移动相关的开销（而非通信本身）对整体执行时间有巨大影响。在第一种分析中，我们引入了两个新的指标，用于测量工作负载运行过程中的算法效率。第二种分析是从被平流粒子的角度进行的。使用以粒子为中心的分析，我们发现与进程间粒子移动相关的开销对整体执行时间有巨大影响。当流特征跨越多个块时，这些开销变得尤为昂贵，导致粒子在块之间反复循环。
英文摘要：
Particle advection is one of the foundational algorithms for visualization and analysis and is central to understanding vector fields common to scientific simulations. Achieving efficient performance with large data in a distributed memory setting is notoriously difficult. Because of its simplicity and minimized movement of large vector field data, the Parallelize over Data (POD) algorithm has become a de facto standard. Despite its simplicity and ubiquitous usage, the scaling issues with the POD algorithm are known and have been described throughout the literature. In this paper, we describe a set of in-depth analyses of the POD algorithm that shed new light on the underlying causes for the poor performance of this algorithm. We designed a series of representative workloads to study the performance of the POD algorithm and executed them on a supercomputer while collecting timing and statistical data for analysis. We then performed two different types of analysis. In the first analysis, we introduce two novel metrics for measuring algorithmic efficiency over the course of a workload run. The second analysis was from the perspective of the particles being advected. Using particle centric analysis, we identify that the overheads associated with particle movement between processes (not the communication itself) have a dramatic impact on the overall execution time. In the first analysis, we introduce two novel metrics for measuring algorithmic efficiency over the course of a workload run. The second analysis was from the perspective of the particles being advected. Using particle-centric analysis, we identify that the overheads associated with particle movement between processes have a dramatic impact on the overall execution time. These overheads become particularly costly when flow features span multiple blocks, resulting in repeated particle circulation between blocks.
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。
