Performance analysis and optimization of AMGA for the large-scale virtual screening

Sunil Ahn, Namgyu Kim, Seehoon Lee, Dukyun Nam, Soonwook Hwang, Birger Koblitz, Vincent Breton, Sangyong Han

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper addresses performance issues on ARDA Metadata Grid Application (AMGA) and presents new techniques to improve the throughput of AMGA for the WISDOM environment. The first issue is a performance degradation problem when AMGA is used as a metadata service for task retrieval in the WISDOM environment. To deal with the issue, a new AMGA operation designed to reduce the communication overhead required to retrieve a task from AMGA is proposed. According to a performance study conducted with the new operation, the throughput of task retrieval using the proposed operation can be as much as 70 times higher than the throughput when using the existing AMGA operations. The second issue is an AMGA throughput issue in large-scale grid-enabled applications such as WISDOM, where it is not uncommon that thousands of jobs running on grid nodes access the AGMA service simultaneously. To address this issue, integration of a load-balancing technique and a DB connection pool technique into the AMGA are proposed.Tet results demonstrate that the performance can be improved linearly in proportion to the number of AMGA servers set up for load balancig; the performance improvement continues until the performance limit of the backend database system is reached. &2009 John Wiley & Sons, Ltd.

Original languageEnglish
Pages (from-to)1055-1072
Number of pages18
JournalSoftware - Practice and Experience
Volume39
Issue number12
DOIs
StatePublished - 25 Aug 2009

Keywords

  • AMGA
  • Large-scale grids
  • Metadata catalog
  • Virtual screening
  • WISDOM

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