scholarly journals Ab Initio potential grid based docking: From High Performance Computing to In Silico Screening

2007 ◽  
Author(s):  
Marc R. de Jonge ◽  
H. Maarten Vinkers ◽  
Joop H. van Lenthe ◽  
Frits Daeyaert ◽  
Ian J. Bush ◽  
...  
Keyword(s):  
Ab Initio ◽  
High Performance Computing ◽  
In Silico ◽  
High Performance ◽  
In Silico Screening ◽  
Performance Computing ◽  
Grid Based

scholarly journals Editorial: Linking experimental and computational connectomics

2019 ◽  
Vol 3 (4) ◽  
pp. 902-904
Author(s):  
Alexander Peyser ◽  
Sandra Diaz Pier ◽  
Wouter Klijn ◽  
Abigail Morrison ◽  
Jochen Triesch
Keyword(s):  
High Performance Computing ◽  
In Silico ◽  
High Performance ◽  
Large Scale ◽  
Generative Models ◽  
Anatomical Structure ◽  
Global Function ◽  
Theoretical Neuroscience ◽  
New Generation ◽  
Performance Computing

Large-scale in silico experimentation depends on the generation of connectomes beyond available anatomical structure. We suggest that linking research across the fields of experimental connectomics, theoretical neuroscience, and high-performance computing can enable a new generation of models bridging the gap between biophysical detail and global function. This Focus Feature on ”Linking Experimental and Computational Connectomics” aims to bring together some examples from these domains as a step toward the development of more comprehensive generative models of multiscale connectomes.

scholarly journals Grid based high performance computing in satellite imagery. Case study - Perona-Malik filter

2015 ◽  
Vol 7 (3) ◽  
pp. 399-406 ◽  
Author(s):  
Bogdan Belean ◽  
Carmen Belean ◽  
Calin Gabriel Floare ◽  
Codruta Mihaela Varodi ◽  
Adrian Bot ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Satellite Imagery ◽  
High Performance ◽  
Performance Computing ◽  
Grid Based

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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