Over the past few years, various international groups have initiated research in the area of parallel and distributed computing in order to provide scientists with new programming methodologies that are required by state-of-the-art scientific application domains. These methodologies target collaborative, multidisciplinary, interactive, and large-scale applications that access a variety of high-end resources shared with others. | 26 Commodity Grid kits - middleware for building Grid computing environments Gregor von Laszewski 1 Jarek Gawor 1 Sriram Krishnan 1 3 and Keith Jackson2 1 Argonne National Laboratory Argonne Illinois United States 2Lawrence Berkeley National Laboratory Berkeley California United States 3Indiana University Bloomington Indiana United States INTRODUCTION Over the past few years various international groups have initiated research in the area of parallel and distributed computing in order to provide scientists with new programming methodologies that are required by state-of-the-art scientific application domains. These methodologies target collaborative multidisciplinary interactive and large-scale applications that access a variety of high-end resources shared with others. This research has resulted in the creation of computational Grids. The term Grid has been popularized during the past decade and denotes an integrated distributed computing infrastructure for advanced science and engineering applications. Grid Computing - Making the Global Infrastructure a Reality. Edited by F. Berman A. Hey and G. Fox 2003 John Wiley Sons Ltd ISBN 0-470-85319-0 640 GREGOR VON LASZEWSKI ETAL. The concept of the Grid is based on coordinated resource sharing and problem solving in dynamic multi-institutional virtual organizations 1 . In addition to providing access to a diverse set of remote resources located at different organizations Grid computing is required to accommodate numerous computing paradigms ranging from client-server to peer-to-peer computing. High-end applications using such computational Grids include data- compute- and network-intensive applications. Application examples range from nanomaterials 2 structural biology 3 and chemical engineering 4 to high-energy physics and astrophysics 5 . Many of these applications require the coordinated use of real-time large-scale instrument and experiment handling distributed data sharing among hundreds or even thousands of .
