Title:	Performance Assessment of Diffusive Load Balancing for Distributed Particle Advection
Authors:	Demiralp, Ali Can Helmrich, Dirk Norbert Protze, Joachim Kuhlen, Torsten Wolfgang Gerrits, Tim
Citation:	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 6-15.
Issue Date:	2022
Publisher:	Václav Skala - UNION Agency
Document type:	conferenceObject
URI:	http://hdl.handle.net/11025/49573
ISBN:	978-80-86943-33-6
ISSN:	2464-4617
Keywords:	částicová advekce;distribuované algoritmy;vyvažování zátěže
Keywords in different language:	particle advection;distributed algorithms;load balancing
Abstract in different language:	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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2022: Full Papers Proceedings

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