High-Performance Computing, Multi-Scale Models for Crystal Growth Systems

2002 ◽  
pp. 185-200 ◽  
Author(s):  
J. J. Derby ◽  
P. Daoutidis ◽  
Y. Kwon ◽  
A. Pandy ◽  
P. Sonda ◽  
...  
Keyword(s):  
Crystal Growth ◽  
High Performance Computing ◽  
High Performance ◽  
Multi Scale ◽  
Scale Models ◽  
Performance Computing

scholarly journals Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models

Cell Systems ◽  
2017 ◽  
Vol 4 (2) ◽  
pp. 194-206.e9 ◽  
Author(s):  
Nick Jagiella ◽  
Dennis Rickert ◽  
Fabian J. Theis ◽  
Jan Hasenauer
Keyword(s):  
High Performance Computing ◽  
Statistical Inference ◽  
High Performance ◽  
Multi Scale ◽  
Scale Models ◽  
Performance Computing

scholarly journals High performance computing in multi-scale modeling, graph science and meta-heuristic optimization

2016 ◽  
Vol 10 (1) ◽  
pp. 50-70
Author(s):  
M. Ivanovic ◽  
B. Stojanovic ◽  
V. Simic ◽  
A. Malisic-Kaplarevic ◽  
V. Rankovic ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Heuristic Optimization ◽  
Multi Scale ◽  
Scale Modeling ◽  
Multi Scale Modeling ◽  
Performance Computing

scholarly journals Multi-scale Problems, High Performance Computing and Hybrid Numerical Methods

2014 ◽  
pp. 245-255
Author(s):  
G. Balarac ◽  
G. -H. Cottet ◽  
J. -M. Etancelin ◽  
J. -B. Lagaert ◽  
F. Perignon ◽  
...  
Keyword(s):  
Numerical Methods ◽  
High Performance Computing ◽  
High Performance ◽  
Multi Scale ◽  
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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