In-kernel integration of operating system and infiniband functions for high performance computing clusters: a DSM example

2005 ◽  
Vol 16 (9) ◽  
pp. 830-840 ◽  
Author(s):  
L. Liss ◽  
Y. Birk ◽  
A. Schuster
Keyword(s):  
Operating System ◽  
High Performance Computing ◽  
High Performance ◽  
Kernel Integration ◽  
Performance Computing

scholarly journals Análise de Características Comportamentais de Aplicações OpenMP para Redução do Consumo de Energia

2018 ◽  
Author(s):  
Gabriel B. Moro ◽  
Lucas Mello Schnorr
Keyword(s):  
Operating System ◽  
Energy Consumption ◽  
High Performance Computing ◽  
High Performance ◽  
Previous Analysis ◽  
Computer Systems ◽  
Specific Knowledge ◽  
Reduce Energy Consumption ◽  
Performance Computing ◽  
Processor Frequency

Performance and energy consumption are fundamental requirements in computer systems. A very frequent challenge is to combine both aspects, searching to keep the high performance computing while consuming less energy. There are a lot of techniques to reduce energy consumption, but in general, they use modern processors resources or they require specific knowledge about application and platform used. In this paper, we propose a library that dynamically changes the processor frequency according to the application's computing behavior, using a previous analysis of its Memory-Bound regions. The results show a reduction of 1,89% in energy consumption for Lulesh application with an increase of 0,09% in runtime when we compare our approach against the governor Ondemand of the Linux Operating System.

scholarly journals Computational physics Using Python: Implementing Maxwell Equation for Circle Polarization

2011 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
M Madlazim ◽  
Bagus Jaya Santosa
Keyword(s):  
Operating System ◽  
High Performance Computing ◽  
Program Development ◽  
Maxwell Equation ◽  
Computer Algebra ◽  
High Performance ◽  
Computational Physics ◽  
On Line ◽  
Performance Computing ◽  
The Web

Python is a relatively new computing language, created by Guido van Rossum [A.S. Tanenbaum, R. van Renesse, H. van Staveren, G.J. Sharp, S.J. Mullender, A.J. Jansen, G. van Rossum, Experiences with the Amoeba distributed operating system, Communications of the ACM 33 (1990) 46–63; also on-line at http://www.cs.vu.nl/pub/amoeba/, which is particularly suitable for teaching a course in computational physics. There are two questions to be considered: (i) For whom is the course intended? (ii) What are the criteria for a suitable language, and why choose Python? The criteria include the nature of the application. High performance computing requires a compiled language, e.g., FORTRAN. For some applications a computer algebra, e.g., Maple, is appropriate. For teaching, and for program development, an interpreted language has considerable advantages: Python appears particularly suitable. Python‟s attractions include (i) its system of modules which makes it easy to extend, (ii) its excellent graphics (VPython module), (iii) its excellent on line documentation, (iv) it is free and can be downloaded from the web. Python and VPython will be described briefly, and some programs demonstrated numerical and animation of some phenomenal physics. In this article, we gave solution of circle polarization by solving Maxwell equation.

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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