A study of scientific visualization on heterogeneous processors using Legion

Lina Yu, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a study of scientific visualization on heterogeneous processors using the Legion runtime system. We describe the main functions in our approach to conduct scientific visualization that can consist of multiple operations with different data requirements. Our approach can help users simplify programming on the data partition, data organization and data movement for distributed-memory heterogeneous architectures, thereby facilitating a simultaneous execution of multiple operations on modern and future supercomputers. We demonstrate the scalable performance and the easy usage of our approach by a hybrid data partitioning and distribution scheme for different data types using both CPUs and GPUS on a heterogeneous system.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings
EditorsKenneth Moreland, Markus Hadwiger, Ross Maciejewski
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages107-108
Number of pages2
ISBN (Electronic)9781509056590
DOIs
Publication statusPublished - Mar 8 2017
Event6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016 - Baltimore, United States
Duration: Oct 23 2016 → …

Publication series

NameIEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings

Other

Other6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016
CountryUnited States
CityBaltimore
Period10/23/16 → …

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ASJC Scopus subject areas

  • Computer Science Applications
  • Modeling and Simulation

Cite this

Yu, L., & Yu, H. (2017). A study of scientific visualization on heterogeneous processors using Legion. In K. Moreland, M. Hadwiger, & R. Maciejewski (Eds.), IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings (pp. 107-108). [7874341] (IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV.2016.7874341