Building a scientific data grid with D i GS

2009 ◽  
Vol 367 (1897) ◽  
pp. 2471-2481 ◽  
Author(s):  
Mark G. Beckett ◽  
Chris R. Allton ◽  
Christine T.H. Davies ◽  
Ilan Davis ◽  
Jonathan M. Flynn ◽  
...  
Keyword(s):  
Molecular Biology ◽  
High Performance Computing ◽  
Particle Physics ◽  
High Performance ◽  
Scientific Data ◽  
Grid Technology ◽  
Data Infrastructure ◽  
Software Application ◽  
Performance Computing ◽  
Insight Into

We provide an insight into the challenge of building and supporting a scientific data infrastructure with reference to our experience working with scientists from computational particle physics and molecular biology. We illustrate how, with modern high-performance computing resources, even small scientific groups can generate huge volumes (petabytes) of valuable scientific data and explain how grid technology can be used to manage, publish, share and curate these data. We describe the D i GS software application, which we have developed to meet the needs of smaller communities and we have highlighted the key elements of its functionality.

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

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