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<p><b>Abstract</b>—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.</p>
Data intensive computing, access pattern, storage pattern, MDMS.

G. Memik, M. Kandemir, A. Choudhary, W. Liao and X. Shen, "A High-Performance Application Data Environment for Large-Scale Scientific Computations," in IEEE Transactions on Parallel & Distributed Systems, vol. 14, no. , pp. 1262-1274, 2003.
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