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Design and Implementation of Memory-Centric Computing System for Big Data Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(7), pp.1-7
  • DOI : 10.9708/jksci.2022.27.07.001
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : May 26, 2022
  • Accepted : July 21, 2022
  • Published : July 29, 2022

Byung-Kwon Jung 1

1한국전자통신연구원

Accredited

ABSTRACT

Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.