Parallel Optimization Algorithms for Multi-Body Systems in High Performance Computing and Networking

2000 ◽  
Author(s):  
Beidi S. Hamma ◽  
José M. Jiménez ◽  
Nassouh A. Chehayeb ◽  
Luc Giraud
Keyword(s):  
Global Optimization ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Algorithms ◽  
Optimal Designs ◽  
Parallel Optimization ◽  
Global Optimization Algorithm ◽  
Definition Of ◽  
Multi Body ◽  
Performance Computing

Abstract In this paper we present some parallel optimization experiments achieved in ODESIM project, a High Performance Computing and Networking (HPCN) project funded by the European Commission within ESPRIT programme. The project acronym stands for Optimum DESIgn of Multi-Body systems. The project main objective has been the development of a set of software tools for finding optimal designs of MBS for kinematic and dynamic simulation. One part of the work in this project was the definition of parallel optimization algorithms suitable for MBS (Multi-Body Systems) and their implementation in a HPCN framework. In this paper we will present local optimization algorithms as well as some simple global optimization algorithm for MBS simulation.

scholarly journals High Performance Computing for Optimum Design of Multi-body Systems

1999 ◽  
pp. 1199-1202
Author(s):  
José M. Jiménez ◽  
Nassouh A. Chehayeb ◽  
Javier G. Izaguirre ◽  
Beidi Hamma ◽  
Yan Thiaudière
Keyword(s):  
High Performance Computing ◽  
Optimum Design ◽  
High Performance ◽  
Multi Body ◽  
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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