Towards Informed Partitioning for Load Balancing: a Proof-of-Concept

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. In this paper, we illustrate informed partitioning by proposing a new geometric partitioning technique for particles simulations. This technique is derived from the wellknown 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 partition∗Corresponding author Email address: anthony.boulmier@unige.ch Preprint submitted to Elsevier August 26, 2021 ar X iv :2 10 8. 11 09 9v 1 [ cs .D C ] 2 5 A ug 2 02 1 ing 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.