Authors:
H. M. Faheem
1
and
B. König-Ries
2
Affiliations:
1
Ain Shams University, Egypt
;
2
Jena University, Germany
Keyword(s):
Bioinformatics, Heterogeneous Architectures, Motif Finding Problem and Multiagent Systems.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Intelligent Agents
;
Internet Technology
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
The exponential increase of the amount of data available in several domains and the need for processing such data makes problems become computationally intensive. Consequently, it is infeasible to carry out sequential analysis, so the need for parallel processing. Over the last few years, the widespread deployment of multicore architectures, accelerators, grids, clusters, and other powerful architectures such as FPGAs and ASICs has encouraged researchers to write parallel algorithms using available parallel computing paradigms to solve such problems. The major challenge now is to take advantage of these architectures irrespective of their heterogeneity. This is due to the fact that designing an execution model that can unify all computing resources is still very difficult. Moreover, scheduling tasks to run efficiently on heterogeneous architectures still needs a lot of research. Existing solutions tend to focus on individual architectures or deal with heterogeneity among CPUs and GP
Us only, but in reality, often, heterogeneous systems exist. Up to now very cumbersome, manual adaption is required to take advantage of these heterogeneous architectures. The aim of this paper is to provide a proposal for a functional-level design of a multiagent-based framework to deal with the heterogeneity of hardware architectures and parallel computing paradigms deployed to solve those problems. Bioinformatics will be selected as a case study.
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