Grid Computing for High Performance Computing (HPC) Data Centers

2007 ◽  
Author(s):  
Virginia W. Ross ◽  
Scott E. Spetka
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Data Centers ◽  
Performance Computing

scholarly journals Merging Plasmonics and Silicon Photonics Towards Greener and Faster “Network-on-Chip” Solutions for Data Centers and High-Performance Computing Systems

10.5772/51853 ◽  
2012 ◽  
Author(s):  
Sotirios Papaioannou ◽  
Konstantinos Vyrsokinos ◽  
Dimitrios Kalavrouziotis ◽  
Giannis Giannoulis ◽  
Dimitrios Apostolopoulos ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Silicon Photonics ◽  
High Performance ◽  
Data Centers ◽  
Network On Chip ◽  
Computing Systems ◽  
On Chip ◽  
Performance Computing

On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

2012 ◽  
pp. 841-861
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Cluster Computing ◽  
Structural Features ◽  
Performance Technology ◽  
Data Intensive ◽  
Computing Platforms ◽  
Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

2011 ◽  
Vol 3 (1) ◽  
pp. 69-88
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Cluster Computing ◽  
Structural Features ◽  
Biological Data ◽  
Performance Technology ◽  
Computing Platforms ◽  
Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

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