A high-performance application data environment for large-scale scientific computations

Xiaohui Shen*, Wei-Keng Liao, Alok Nidhi Choudhary, Gokhan Memik, Mahmut Kandemir

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Effective high-level data management is becoming an important issue with more and more scientific applications manipulating huge amounts of secondary-storage and tertiary-storage data using parallel processors. A major problem facing the current solutions to this data management problem is that these solutions either require a deep understanding of specific data storage architectures and file layouts to obtain the best performance (as in high-performance storage management systems and parallel file systems), or they sacrifice significant performance in exchange for ease-of-use and portability (as in traditional database management systems). In this paper, we discuss the design, implementation, and evaluation of a novel application development environment for scientific computations. This environment includes a number of components that make it easy for the programmers to code and run their applications without much programming effort and, at the same time, to harness the available computational and storage power on parallel architectures.

Original languageEnglish (US)
Pages (from-to)1262-1274
Number of pages13
JournalIEEE Transactions on Parallel and Distributed Systems
Volume14
Issue number12
DOIs
StatePublished - Dec 2003

Keywords

  • Access pattern
  • Data intensive computing
  • MDMS
  • Storage pattern

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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