scholarly journals A Fully Parameterized Virtual Coarse Grained Reconfigurable Array for High Performance Computing Applications

2016 ◽  
Author(s):  
Amit Kulkarni ◽  
Elias Vasteenkiste ◽  
Dirk Stroobandt ◽  
Andreas Brokalakis ◽  
Antonios Nikitakis
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Coarse Grained ◽  
Reconfigurable Array ◽  
Performance Computing

Grids, Clouds, and Massive Simulations

2014 ◽  
pp. 308-340
Author(s):  
Levente Hajdu ◽  
Jérôme Lauret ◽  
Radomir A. Mihajlović
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Nuclear Physics ◽  
High Performance ◽  
National Laboratory ◽  
Brookhaven National Laboratory ◽  
Department Of Energy ◽  
Coarse Grained ◽  
Fine Grained ◽  
Performance Computing

In this chapter, the authors discuss issues surrounding High Performance Computing (HPC)-driven science on the example of Peta science Monte Carlo experiments conducted at the Brookhaven National Laboratory (BNL), one of the US Department of Energy (DOE) High Energy and Nuclear Physics (HENP) research sites. BNL, hosting the only remaining US-based HENP experiments and apparatus, seem appropriate to study the nature of the High-Throughput Computing (HTC) hungry experiments and short historical development of the HPC technology used in such experiments. The development of parallel processors, multiprocessor systems, custom clusters, supercomputers, networked super systems, and hierarchical parallelisms are presented in an evolutionary manner. Coarse grained, rigid Grid system parallelism is contrasted by cloud computing, which is classified within this chapter as flexible and fine grained soft system parallelism. In the process of evaluating various high performance computing options, a clear distinction between high availability-bound enterprise and high scalability-bound scientific computing is made. This distinction is used to further differentiate cloud from the pre-cloud computing technologies and fit cloud computing better into the scientific HPC.

High performance computing on graphics processing units

2008 ◽  
Vol 3 (2) ◽  
pp. 27-34 ◽  
Author(s):  
Balázs Tukora ◽  
Tibor Szalay
Keyword(s):  
High Performance Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Graphics Processing ◽  
Performance Computing

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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