Moha: A composable system for efficient in-situ analytics on heterogeneous hpc systems

Haoyuan Xing, Gagan Agrawal, Rajiv Ramnath

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

Abstract

Heterogeneous, dense computing architectures consisting of several accelerators, such as GPUs, attached to general-purpose CPUs are now integral High-Performance Computing (HPC) systems. However, these architectures pose severe memory and I/O constraints to computations involving in-situ analytics. This paper introduces MoHA, a framework for in-situ analytics that is designed to efficiently use the limited resources available on heterogeneous platforms. MoHA achieves this efficiency through the extensive use of bitmaps as a compressed or summary representation of simulation outputs. Our specific contributions in this paper include the design of bitmap generation and storage methods suitable for GPUs, the design and efficient implementation of a set of key operators for MoHA, and demonstrations of how several real queries on real datasets can be implemented using these operators. We demonstrate that MoHA reduces I/O transfer as well as overall processing time when compared to a baseline that does not use compressed representations.

Original languageEnglish (US)
Title of host publicationProceedings of SC 2020
Subtitle of host publicationInternational Conference for High Performance Computing, Networking, Storage and Analysis
PublisherIEEE Computer Society
ISBN (Electronic)9781728199986
DOIs
StatePublished - Nov 2020
Event2020 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020 - Virtual, Atlanta, United States
Duration: Nov 9 2020Nov 19 2020

Publication series

NameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
Volume2020-November
ISSN (Print)2167-4329
ISSN (Electronic)2167-4337

Conference

Conference2020 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period11/9/2011/19/20

Keywords

  • Accelerator architecture
  • Data compression
  • High performance computing
  • Indexes
  • Query processing
  • Scientific computing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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