Reducing Data Motion and Energy Consumption of Geospatial Modeling Applications Using Automated Precision Conversion

Qinglei Cao*, Sameh Abdulah, Hatem Ltaief, Marc G. Genton, David Keyes, George Bosilca

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The burgeoning interest in large-scale geospatial modeling, particularly within the domains of climate and weather prediction, underscores the concomitant critical importance of accuracy, scalability, and computational speed. Harnessing these complex simulations' potential, however, necessitates innovative computational strategies, especially considering the increasing volume of data involved. Recent advancements in Graphics Processing Units (GPUs) have opened up new avenues for accelerating these modeling processes. In particular, their efficient utilization necessitates new strategies, such as mixed-precision arithmetic, that can balance the trade-off between computational speed and model accuracy. This paper leverages PaRSEC runtime system and delves into the opportunities provided by mixed-precision arithmetic to expedite large-scale geospatial modeling in heterogeneous environments. By using an automated conversion strategy, our mixed-precision approach significantly improves computational performance (up to 3X) on Summit supercomputer and reduces the associated energy consumption on various Nvidia GPU generations. Importantly, this implementation ensures the requisite accuracy in environmental applications, a critical factor in their operational viability. The findings of this study bear significant implications for future research and development in high-performance computing, underscoring the transformative potential of mixed-precision arithmetic on GPUs in addressing the computational demands of large-scale geospatial modeling and making a stride toward a more sustainable, efficient, and accurate future in large-scale environmental applications.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE International Conference on Cluster Computing, CLUSTER 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages330-342
Number of pages13
ISBN (Electronic)9798350307924
DOIs
StatePublished - 2023
Event25th IEEE International Conference on Cluster Computing, CLUSTER 2023 - Santa Fe, United States
Duration: Oct 31 2023Nov 3 2023

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)1552-5244

Conference

Conference25th IEEE International Conference on Cluster Computing, CLUSTER 2023
Country/TerritoryUnited States
CitySanta Fe
Period10/31/2311/3/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Automated precision conversion
  • Geospatial statistics
  • GPU acceleration
  • HPC
  • Task-based runtime

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Signal Processing

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