Contextual Abstraction in a Type System for Component-Based High Performance Computing Platforms

2013 ◽  
pp. 90-104 ◽  
Author(s):  
Francisco Heron de Carvalho Junior ◽  
Cenez Araújo Rezende ◽  
Jefferson de Carvalho Silva ◽  
Wagner Guimarães Al-Alam
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Type System ◽  
Computing Platforms ◽  
Performance Computing

scholarly journals Contextual abstraction in a type system for component-based high performance computing platforms

2016 ◽  
Vol 132 ◽  
pp. 96-128 ◽  
Author(s):  
Francisco Heron de Carvalho Junior ◽  
Cenez Araújo Rezende ◽  
Jefferson de Carvalho Silva ◽  
Wagner Guimarães Al-Alam ◽  
João Marcelo Uchoa de Alencar
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Type System ◽  
Computing Platforms ◽  
Performance Computing

Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms

2015 ◽  
Vol 11 ◽  
pp. 69-81 ◽  
Author(s):  
Emad Elsebakhi ◽  
Frank Lee ◽  
Eric Schendel ◽  
Anwar Haque ◽  
Nagarajan Kathireason ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Functional Networks ◽  
Computing Platforms ◽  
Performance Computing

scholarly journals Investigating methods of supporting dynamically linked executables on high performance computing platforms.

2009 ◽  
Author(s):  
Suzanne Marie Kelly ◽  
James H., III Laros ◽  
Kevin Thomas Tauke Pedretti ◽  
Michael J. Levenhagen
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing Platforms ◽  
Performance Computing

scholarly journals IDEA OF ARCHITECTURE OF A MULTIPROCESSOR COMPUTER M-10 AND THEIR IMPLEMENTATION IN MODERN COMPUTING PLATFORMS (IN SMALL FORMS)

2019 ◽  
pp. 28-31
Author(s):  
E. V. Glivenko ◽  
S. А. Sorokin ◽  
G. N. Petrovа
Keyword(s):  
Parallel Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Multiprocessor Computer ◽  
M 10 ◽  
Operator Type ◽  
Computing Platforms ◽  
Main Ideas ◽  
New Generation ◽  
Performance Computing

The article is devoted to the design of high‑performance computing devices for parallel processing of information. The problem of  increasing the productivity of computing facilities by one or several orders of magnitude is considered on the example of the high‑ performance electronic computer M‑10, which was created in the 1970s at the NIIVK. If in a conventional computer, the method  of processing numbers is given by commands, then in M‑10, the methods for processing a function were specified by operators  taken from functional analysis. At the same time, the possibility of parallel processing of an entire information line appeared. Such  systems began to be called «functional operator type machines». The main ideas presented in the article may be of interest to  developers of specialized machines of the new generation, as well as engineers involved in the creation of high‑performance  computing devices using technologies of computing platforms.

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.

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