skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing

Abstract

We present a novel dynamic load-balancing algorithm based on data repartitioning for parallel particle tracing in flow visualization. Instead of static data assignment, we dynamically repartition the data into blocks and reassign the blocks to processes to balance the workload distribution among the processes. Block repartitioning is performed based on a dynamic workload estimation method that predicts the workload in the flow field on the fly as the input. In our approach, we allow data duplication in the repartitioning, enabling the same data blocks to be assigned to multiple processes. Load balance is achieved by regularly exchanging the blocks (together with the particles in the blocks) among processes according to the output of the data repartitioning. Compared with other load-balancing algorithms, our approach does not need any preprocessing on the raw data and does not require any dedicated process for work scheduling, while it has the capability to balance uneven workload efficiently. Results show improved load balance and high efficiency of our method on tracing particles in both steady and unsteady flow.

Authors:
; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science - Office of Advanced Scientific Computing Research; National Natural Science Foundation of China (NNSFC); National Key Basic Research Program of China; National Key Research and Development Program of China
OSTI Identifier:
1467433
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 11th IEEE Pacific Visualization Symposium, 04/10/18 - 04/13/18, Kobe, JP
Country of Publication:
United States
Language:
English
Subject:
Parallel particle tracing; data repartitioning; dynamic load balancing

Citation Formats

Zhang, Jiang, Guo, Hanqi, Yuan, Xiaoru, and Peterka, Tom. Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing. United States: N. p., 2018. Web. doi:10.1109/PacificVis.2018.00019.
Zhang, Jiang, Guo, Hanqi, Yuan, Xiaoru, & Peterka, Tom. Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing. United States. doi:10.1109/PacificVis.2018.00019.
Zhang, Jiang, Guo, Hanqi, Yuan, Xiaoru, and Peterka, Tom. Mon . "Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing". United States. doi:10.1109/PacificVis.2018.00019.
@article{osti_1467433,
title = {Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing},
author = {Zhang, Jiang and Guo, Hanqi and Yuan, Xiaoru and Peterka, Tom},
abstractNote = {We present a novel dynamic load-balancing algorithm based on data repartitioning for parallel particle tracing in flow visualization. Instead of static data assignment, we dynamically repartition the data into blocks and reassign the blocks to processes to balance the workload distribution among the processes. Block repartitioning is performed based on a dynamic workload estimation method that predicts the workload in the flow field on the fly as the input. In our approach, we allow data duplication in the repartitioning, enabling the same data blocks to be assigned to multiple processes. Load balance is achieved by regularly exchanging the blocks (together with the particles in the blocks) among processes according to the output of the data repartitioning. Compared with other load-balancing algorithms, our approach does not need any preprocessing on the raw data and does not require any dedicated process for work scheduling, while it has the capability to balance uneven workload efficiently. Results show improved load balance and high efficiency of our method on tracing particles in both steady and unsteady flow.},
doi = {10.1109/PacificVis.2018.00019},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: