A High-Performance Computational Framework for BionetworkAnalysis

Author

Chin, George ; Chavarria, Daniel G. ; Nakamura, Grant C. ; Sofia, Heidi J.

fYear

2007

fDate

13-15 Aug. 2007

Firstpage

100

Lastpage

107

Abstract

We introduce BioGraphE, a general, scaleable integration platform for connecting graph problems in biology to computational solvers and high-performance systems. This framework will enable computational scientists to identify and bring in graph analysis applications and to easily connect them to efficient and powerful computational software and hardware that are specifically designed and tuned to solve complex graph problems. We investigate the use of a Boolean satisfiability solver known as Survey Propagation as a core computational solver and high-performance parallel systems that utilize multithreaded processor architectures.

Keywords

biology computing; computability; computational complexity; graph theory; microprocessor chips; multi-threading; parallel architectures; BioGraphE; Boolean satisfiability solver; Survey Propagation; biology; bionetwork analysis; graph analysis; graph problems; multithreaded processor architectures; parallel systems; Bioinformatics; Biological cells; Biological information theory; Biological systems; Biology computing; Cells (biology); Computer networks; Databases; Genomics; Proteins;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Computational Sciences, 2007. IMSCCS 2007. Second International Multi-Symposiums on

Conference_Location

Iowa City, IA

Print_ISBN

978-0-7695-3039-0

Type

conf

DOI

10.1109/IMSCCS.2007.8

Filename

4392586