DDS: integrating data analytics transformations in task-based workflows

High-performance data analytics (HPDA) is a current trend in e-science research that aims to integrate traditional HPC with recent data analytic frameworks. Most of the work done in this field has focused on improving data analytic frameworks by implementing their engines on top of HPC technologies...

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Bibliographic Details
Published in:Open research Europe Vol. 2; p. 66
Main Authors: Mammadli, Nihad, Ejarque, Jorge, Alvarez, Javier, Badia, Rosa M.
Format: Journal Article
Language:English
Published: London, UK F1000 Research Limited 01.01.2022
F1000 Research Ltd
Subjects:
Big Data High Performance
Data Analytics
eng
Parallel Computing
Task Based Programming Models
ISSN:2732-5121, 2732-5121
Online Access:Get full text
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Summary:High-performance data analytics (HPDA) is a current trend in e-science research that aims to integrate traditional HPC with recent data analytic frameworks. Most of the work done in this field has focused on improving data analytic frameworks by implementing their engines on top of HPC technologies such as Message Passing Interface. However, there is a lack of integration from an application development perspective. HPC workflows have their own parallel programming models, while data analytic (DA) algorithms are mainly implemented using data transformations and executed with frameworks like Spark. Task-based programming models (TBPMs) are a very efficient approach for implementing HPC workflows. Data analytic transformations can also be decomposed as a set of tasks and implemented with a task-based programming model. In this paper, we present a methodology to develop HPDA applications on top of TBPMs that allow developers to combine HPC workflows and data analytic transformations seamlessly. A prototype of this approach has been implemented on top of the PyCOMPSs task-based programming model to validate two aspects: HPDA applications can be seamlessly developed and have better performance than Spark. We compare our results using different programs. Finally, we conclude with the idea of integrating DA into HPC applications and evaluation of our method against Spark.
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No competing interests were disclosed.
ISSN:2732-5121
2732-5121
DOI:10.12688/openreseurope.14569.2