Parallel Programming Models for Cloud Computing

Editors' Choice

Prof. Heba Fadhil

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Corresponding Author

Affiliation: University of Baghdad, Baghdad, Iraq

Tel: 773

Phone: 2026138

E-mail: fadhilheba@gmail.com

2nd e-mail: heba@kecbu.uobaghdad.edu.iq

Prof. Heba Fadhil's publications in ICSES

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• Task Scheduling in the Cloud Amended Through Genetic Algorithms
  ICSES Transactions on Evolutionary and Metaheuristic Algorithms
  Miscellaneous | Vol. 6, No. 1 | Pages 1-2 | Apr. 2020
  Heba Fadhil
• High Performance Clustering to Improve Data Mining Techniques
  Frontiers of Supercomputing
  Miscellaneous | Vol. 3, No. 1 | Pages 1-2 | Apr. 2020
  Heba Fadhil
• Parallel Programming Models for Cloud Computing
  ICSES Interdisciplinary Transactions on Cloud Computing, IoT, and Big Data
  Special Issue Proposal | In Press | Pages 1-4 | Mar. 2020
  Heba Fadhil
• Private Cloud Data Storage Using Raspberry Pi
  ICSES Interdisciplinary Transactions on Cloud Computing, IoT, and Big Data
  Short Letter | Vol. 3, No. 3 | Pages 1-5 | Sep. 2019
  Heba Fadhil
• The Age of Miraculous Technology Advent to Eradicate Cancer Cells
  ICSES Transactions on Image Processing and Pattern Recognition
  Miscellaneous | Vol. 4, No. 2 | Pages 1-3 | Jun. 2018
  Heba Fadhil
• Cloud Precept: Storage, Backup, and Synchronization
  ICSES Interdisciplinary Transactions on Cloud Computing, IoT, and Big Data
  Short Letter | Vol. 2, No. 1 | Pages 1-3 | Mar. 2018
  Heba Fadhil

Highlights and Novelties

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1- Firstly, this special issue aims at attracting high-quality papers that describe state-of-the-art technologies, new findings and also the applications of cloud computing.

2- Secondly, it describes cutting-edge coverage of new developments in this regard e.g. novel execution models, innovative systems, and implementations.

3- Thirdly, it provides efficient algorithms, software tools and comprehensive data analysis pipelines for the preprocessing of data.

Manuscript Abstract

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The awareness of cloud computing changed the way of using computing resources. Since high performance computing (HPC) has long suffered from excessive use of resources, many HPC researchers are trying to adapt HPC applications to the cloud environment. Through applicable adaptation, HPC applications are able to improve their resource utilization and scalability through the use of virtual resources and upon-demand. Whereas discussing HPC over clouds, we should discuss parallel programming models as well. Diverse parallel programming models and frameworks (such as MPI, OpenMP, OpenCL, CUDA, and MapReduce) are suggested for parallel computing. For example, the MapReduce programming model has been used for a lot of large data-processing applications because it helps reduce the complexity of balancing problems such as decomposition, connectivity, and scheduling. On the other hand, a parallel paradigm programming model for HPC applications must be chosen to achieve high-performance and efficient resource use because the target device constructs are different as well as levels of abstraction. Additionally, as traditional parallel programming paradigms, such as MPI, are implemented for a single leased cluster environment, applying these paradigms to HPC applications on the cloud is a challenge in terms of resource management. This Special Issue’s objectives include but are not limited to novel programming models and frameworks, or extensions of existing programming models, to ease offloading and parallelization of computation to cloud computing for emerging scientific computing applications; novel architectural concepts to boost software execution and to overcome scalability issues of current heterogeneous parallel systems; many-core HW/SW design and applications; software engineering, code optimization, and code generation strategies for parallel systems with heterogeneous processors; multi- and many core-aware approaches for large-scale parallel simulations in both implementation and algorithm design, including scalability studies, application parallelization use cases.

Keywords

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 High performance computing   Cloud computing   Parallel programming model 

Copyright and Licence

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© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors. This manuscript is published in Open-Access manner based on the copyright licence of Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0).

Cite this manuscript as

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Heba Fadhil, "Parallel Programming Models for Cloud Computing," ICSES Interdisciplinary Transactions on Cloud Computing, IoT, and Big Data, In Press, pp. 1-4, Mar. 2020.

For External Scientific Databeses

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