On issues concerning Cloud environments in scope of scalable multi-projection methods
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Date
2016
Authors
Moutafis, Byron E.
Filelis-Papadopoulos, Christos K.
Gravvanis, George A.
Morrison, John P.
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Journal ISSN
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Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Over the last decade, Cloud environments have gained significant attention by the scientific community, due to their flexibility in the allocation of resources and the various applications hosted in such environments. Recently, high performance computing applications are migrating to Cloud environments. Efficient methods are sought for solving very large sparse linear systems occurring in various scientific fields such as Computational Fluid Dynamics, N-Body simulations and Computational Finance. Herewith, the parallel multi-projection type methods are reviewed and discussions concerning the implementation issues for IaaS-type Cloud environments are given. Moreover, phenomena occurring due to the "noisy neighbor" problem, varying interconnection speeds as well as load imbalance are studied. Furthermore, the level of exposure of specialized hardware residing in modern CPUs through the different layers of software is also examined. Finally, numerical results concerning the applicability and effectiveness of multi-projection type methods in Cloud environments based on OpenStack are presented.
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Keywords
Cloud computing , Semi-coarsening , Aggregation , Algebraic domain decomposition , High performance computing , Parallel hybrid solver , Sparse and dense matrix computations , Hardware , Linear systems , Virtual machine monitors , Virtual machining , Noise measurement
Citation
Moutafis, B. E., Filelis-Papadopoulos, C. K., Gravvanis, G. A. and Morrison, J. P. (2016) 'On issues concerning Cloud environments in scope of scalable multi-projection methods', 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Timisoara, Romania, 24-27 September. doi:10.1109/SYNASC.2016.061
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© 2016, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.