A Systematic Comparison of Dynamic Load Balancing Algorithms for Massively Parallel Rigid Particle Dynamics

TL;DR

This paper systematically compares six load balancing algorithms for large-scale particle simulations, analyzing their performance, scalability, and efficiency on supercomputers to guide optimal algorithm selection.

Contribution

It provides a comprehensive evaluation of load balancing strategies for massively parallel particle dynamics, including performance metrics and scalability insights.

Findings

Identified the most efficient algorithms for different simulation scales

Analyzed the trade-offs between load balancing quality and runtime costs

Demonstrated scalability up to over one million processes

Abstract

As compute power increases with time, more involved and larger simulations become possible. However, it gets increasingly difficult to efficiently use the provided computational resources. Especially in particle-based simulations with a spatial domain partitioning large load imbalances can occur due to the simulation being dynamic. Then a static domain partitioning may not be suitable. This can deteriorate the overall runtime of the simulation significantly. Sophisticated load balancing strategies must be designed to alleviate this problem. In this paper we conduct a systematic evaluation of the performance of six different load balancing algorithms. Our tests cover a wide range of simulation sizes, and employ one of the largest supercomputers available. In particular we study the runtime and memory complexity of all components of the simulation carefully. When progressing to extreme scale simulations it is essential to identify bottlenecks and to predict the scaling behaviour. Scaling experiments are shown for up to over one million processes. The performance of each algorithm is analyzed with respect to the quality of the load balancing and its runtime costs. For all tests, the waLBerla multiphysics framework is employed.