High Performance Computing: Analytical Aerodynamics for Automotive Vehicles

1999 ◽  
Author(s):  
Ronald H. Miller ◽  
Gary S. Strumolo ◽  
Evangelos Hytopoulos ◽  
Stephen A. Remondi ◽  
Samuel M. Watson
Keyword(s):  
Wind Tunnel ◽  
Parallel Algorithms ◽  
High Performance Computing ◽  
High Performance ◽  
Experimental Testing ◽  
Computer Architectures ◽  
Wind Tunnels ◽  
Cfd Simulations ◽  
Design Time ◽  
Performance Computing

Abstract High Performance Computing (HPC) represents a significant resource whereby automotive manufacturers can utilize analytical methodologies to reduce experimental testing and design time, resulting in lower costs and higher quality. Optimization of styling and aerodynamics requires multiple CFD simulations which have been enabled by the commercial availability of parallel algorithms, as well as enhancements in computer architectures. We have developed a Virtual Aerodynamic Wind Tunnel (VAWT) which uses PowerFLOW® and can simulate conditions similar to experimental wind tunnels. One key element of this methodology is the use of PowerFLOW. Two of the major attributes of PowerFLOW are its inherent parallelization and automeshing capabilities. In this paper, we will focus on the scalability and feasibility of PoweFLOW, which is essential for the optimization of styling and aerodynamics. Timing and scalability results on an Origin 2000 server are presented for a number of different configurations.

Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

2017 ◽  
Vol 46 (3) ◽  
pp. 508-527 ◽  
Author(s):  
Awais Ahmad ◽  
Anand Paul ◽  
Sadia Din ◽  
M. Mazhar Rathore ◽  
Gyu Sang Choi ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Parallel Algorithms ◽  
High Performance Computing ◽  
High Performance ◽  
Multilevel Data ◽  
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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