Implementations and performance of nonlinear CG methods by TAO on Dawning2000+

Author

Wang, Jian ; Chi, Xuebin ; Gu, Tongxiang

Author_Institution

Comput. Network Inf. Center, Chinese Acad. of Sci., Beijing, China

fYear

2004

fDate

20-22 July 2004

Firstpage

252

Lastpage

255

Abstract

Nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger, the dependence on efficient and scalable software is severe. Toolkit for Advanced Optimization (TAO) is a parallel package that can currently solve several kinds of optimization problems. In this paper, we give the framework of several variants of CG: CGFR, CGPR, CGPRP and their implementations in TAO 1.5, which have been tested up to 64 processors on Dawning2000 to solve problems with up to 10 variables. The results show that the scalability of CG implementations in TAO 1.5 is excellent.

Keywords

conjugate gradient methods; mathematical programming; mathematics computing; object-oriented languages; parallel programming; software packages; software tools; CGFR; CGPR; CGPRP; Dawning2000+; TAO 1.5; Toolkit for Advanced Optimization; nonlinear CG methods; nonlinear conjugate gradient methods; parallel package; unconstrained optimization; Character generation; Computer architecture; Computer networks; Design optimization; Gradient methods; Laboratories; Mathematics; Physics computing; Research and development; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

High Performance Computing and Grid in Asia Pacific Region, 2004. Proceedings. Seventh International Conference on

Print_ISBN

0-7695-2138-X

Type

conf

DOI

10.1109/HPCASIA.2004.1324042

Filename

1324042