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International Journal of Parallel Programming
Published in: International Journal of Parallel Programming 3/2018

07-10-2017 | Editorial

Guest Editorial for Programming Models and Algorithms for Data Analysis in HPC Systems

Authors: Salvatore Cuomo, Marco Aldinucci, Massimo Torquati

Published in: International Journal of Parallel Programming | Issue 3/2018

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Excerpt

Performance is still the hottest keyword in parallel and distributed systems: performance evaluation, design for performance, performance portability and scalability are just a few of the many possible declinations that nowadays are of paramount scientific importance. To tackle these challenges, system architects, applications programmers and data center managers need methodological tools to fit at best the overall workload and the available architecture, maximizing the overall performances and minimizing overheads, energy consumption or idle time while application developers mainly aim at algorithmic and software oriented performances. Proper methodologies for modeling and analysis are the way to turn complexity into opportunities. …
next article Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

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Literature
1.
Ahmad, A., Paul, A., Din, S., Rathore, M.M., Choi, G.S., Jeon, G.: Multilevel data processing using parallel algorithms for analyzing big data in high-performance computing. Int. J. Parallel Program. (2017). doi:10.​1007/​s10766-017-0498-x
2.
De Michele, P., Maiorano, F., Marcellino, L., Piccialli, F.: A GPU implementation of OLPCA method in hybrid environment. Int. J. Parallel Program. (2017). doi:10.​1007/​s10766-017-0505-2
3.
Kaur, P.J., Kaushal, S., Sangaiah, A.K., Piccialli, F.: A framework for assessing reusability using package cohesion measure in aspect oriented systems. Int. J. Parallel Program. (2017). doi:10.​1007/​s10766-017-0501-6
4.
Mei, G., Cuomo, S., Tian, H., Xu, N., Peng, L.: Meshcleaner: A generic and straightforward algorithm for cleaning finite element meshes. Int. J. Parallel Program. (2017). doi:10.​1007/​s10766-017-0507-0
5.
Plazolles, B., El Baz, D., Spel, M., Rivola, V., Gegout, P.: SIMD Monte–Carlo numerical simulations accelerated on GPU and xeon phi. Int. J. Parallel Program. (2017). doi:10.​1007/​s10766-017-0509-y
6.
7.
Rathore, M.M., Son, H., Ahmad, A., Paul, A., Jeon, G.: Real-time big data stream processing using GPU with spark over hadoop ecosystem. Int. J. Parallel Program. (2017). doi:10.​1007/​s10766-017-0513-2
Metadata
Title
Guest Editorial for Programming Models and Algorithms for Data Analysis in HPC Systems
Authors
Salvatore Cuomo
Marco Aldinucci
Massimo Torquati
Publication date
07-10-2017
Publisher
Springer US
Published in
International Journal of Parallel Programming / Issue 3/2018
Print ISSN: 0885-7458
Electronic ISSN: 1573-7640
DOI
https://doi.org/10.1007/s10766-017-0531-0

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International Journal of Parallel Programming 3/2018 Go to the issue

OriginalPaper

MeshCleaner: A Generic and Straightforward Algorithm for Cleaning Finite Element Meshes

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Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

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A GPU Implementation of OLPCA Method in Hybrid Environment

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Real-Time Big Data Stream Processing Using GPU with Spark Over Hadoop Ecosystem

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A Framework for Assessing Reusability Using Package Cohesion Measure in Aspect Oriented Systems

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SIMD Monte-Carlo Numerical Simulations Accelerated on GPU and Xeon Phi

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