Performance Assessment of Diffusive Load Balancing for Distributed Particle Advection

TL;DR

This paper introduces two local diffusive load balancing methods for parallel particle advection, reducing runtime and improving scalability by dynamically sharing workloads among neighboring processes.

Contribution

The paper presents novel local diffusive load balancing techniques for particle advection that operate without central control, enhancing scalability and efficiency.

Findings

Methods reduce total run-time of particle advection.

Approaches improve load balance and scalability.

Operate locally on process neighborhoods without central control.

Abstract

Particle advection is the approach for extraction of integral curves from vector fields. Efficient parallelization of particle advection is a challenging task due to the problem of load imbalance, in which processes are assigned unequal workloads, causing some of them to idle as the others are performing compute. Various approaches to load balancing exist, yet they all involve trade-offs such as increased inter-process communication, or the need for central control structures. In this work, we present two local load balancing methods for particle advection based on the family of diffusive load balancing. Each process has access to the blocks of its neighboring processes, which enables dynamic sharing of the particles based on a metric defined by the workload of the neighborhood. The approaches are assessed in terms of strong and weak scaling as well as load imbalance. We show that the methods reduce the total run-time of advection and are promising with regard to scaling as they operate locally on isolated process neighborhoods.