Most parallel applications suffer from load imbalance, a crucial performance degradation factor. In particle simulations, this is mainly due to the migration of particles between processing elements, which eventually gather unevenly and create workload imbalance. Dynamic load balancing is used at various iterations to mitigate load imbalance, employing a partitioning method to divide the computational space evenly while minimizing communications. In this paper, we propose a novel partitioning methodology called ``informed partitioning''. It uses information based on the evolution of the computation to reduce the load balancing growth and the number of load balancing calls. We illustrate informed partitioning by proposing a new geometric partitioning technique for particles simulations. This technique is derived from the well-known recursive coordinate bisection and employs the velocity of the particles to guide the bisection axis. To properly compare the performance of our new method with existing partitioning techniques during application execution, we introduce an effort metric based on a theoretical model of load balanced parallel application time. We propose a proof-of-concept of informed partitioning, through a numerical study, on three N-Body simulations with various particle dynamics, and we discuss its performance against popular geometric partitioning techniques. Moreover, we show that our effort metric can be used to rank partitioning techniques by their efficiency at any time point during the simulation. Eventually, this could be used to choose the best partitioning on the fly. In the numerical study, we report that our novel concept increases the performance of two experiments out of three by up to 76% and 15%, while being marginally slower by only $3\%$ in one experiment. Also, we discuss the limitations of our implementation of informed partitioning and our effort metric.
翻译:在粒子模拟中,这主要是由于粒子在加工元素之间迁移,最终聚集不均,造成工作量不平衡。在各种迭代中使用了动态负平衡,以缓解负不平衡,使用一种平衡方法来平衡计算空间,同时将通信最小化。在本文中,我们提出一个新的分隔方法,称为“知情分隔 ” 。它使用基于计算演进的信息来减少负负平衡增长和负平衡调用量。我们通过提出一种新的粒子模拟几何分解技术来说明知情的分隔。这一技术来自众所周知的递归协调双剖面,并使用粒子速度来引导两截面轴。为了在应用执行期间适当地将我们的新方法的性能与现有的分隔技术作比较,我们采用了一种基于负平衡平行应用时间的理论模型。我们提出了一种基于信息平衡的分化概念,通过一项数字研究来证明知情的分解,在三次N-Body模拟中提出了粒子模拟,我们讨论其运行情况与普观性分解分流轴轴轴轴之间的速度,我们使用了两种计算方法,我们使用这种效率分析方法来测量分流方法来测量。此外,我们使用任何时间分层分析方法,我们使用这种方法来测量分流法的分解方法,我们用一个分解方法,我们使用一种分级法的分级法的分级法,我们用一个分级法,我们用一个分级法的分级法的分级法,我们用一个分级法,我们用来用来用来用来用来做任何顺序的分级法的分级法的分级法,我们用来测量。