A Multiagent-based Framework for Solving Computationally Intensive Problems on Heterogeneous Architectures - Bioinformatics Algorithms as a Case Study

H. M. Faheem, B. König-Ries

2014

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 GPUs 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.

References

  1. Miled, Z. et al., 2003. An Ontology for Semantic Integration of Life Science Web Databases. International Journal of Cooperative Information Systems.12 (02).
  2. Rauber, T., Rünger, G., 2010.Parallel Programming: for Multicore and Cluster Systems.Springer.
  3. Farouk, Y., El-Deeb, T., and Faheem, H., 2011.Massively Parallelized DNA Motif Search on FPGA .Bioinformatics - Trends and Methodologies.INTECH.
  4. Faheem, H. M., 2010. “Associative Memory Array Processor for Solving Motif Finding Problem”. The International Conference on Artificial Intelligence and Applications (AIA). Austria.
  5. Rajasekaran, S., Balla, S. and Huang, C.H., 2005.Exact algorithm for planted motif challenge problems. Proceedings of Asia-Pacific Bioinformatics Conference, 249-259.
  6. Shunmaganathan, K., Deepika, K., Deeba, K., 2008. Agent Based Bioinformatics Integration using RESTINA. The International Arab Journal of Information Technology. 5(3):258-264.
  7. Konwinski, A., 2012.Multi-agent Cluster Scheduling for Scalability and Flexibility. Technical Report No.UCB/EECS-2012-273.
  8. Russel, S., Norvig, P. 1995. Artificial Intellegence - A Modern Approach.Printice-Hall.
  9. Inta, R., Bowman, D., and Scott, M., 2012. The “Chimera”: An Off-The-Shelf CPU / GPGPU / FPGA Hybrid Computing Platform. International Journal of Reconfigurable Computing. Vol. 2012.
Download


Paper Citation


in Harvard Style

M. Faheem H. and König-Ries B. (2014). A Multiagent-based Framework for Solving Computationally Intensive Problems on Heterogeneous Architectures - Bioinformatics Algorithms as a Case Study . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 526-533. DOI: 10.5220/0004967105260533


in Bibtex Style

@conference{iceis14,
author={H. M. Faheem and B. König-Ries},
title={A Multiagent-based Framework for Solving Computationally Intensive Problems on Heterogeneous Architectures - Bioinformatics Algorithms as a Case Study},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2014},
pages={526-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004967105260533},
isbn={978-989-758-027-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Multiagent-based Framework for Solving Computationally Intensive Problems on Heterogeneous Architectures - Bioinformatics Algorithms as a Case Study
SN - 978-989-758-027-7
AU - M. Faheem H.
AU - König-Ries B.
PY - 2014
SP - 526
EP - 533
DO - 10.5220/0004967105260533