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Enabling Scalable Data Processing and Management through Standards-based Job Execution and the Global Federated File System

Enabling Scalable Data Processing and Management through Standards-based Job Execution and the Global Federated File System


Title: Enabling Scalable Data Processing and Management through Standards-based Job Execution and the Global Federated File System
Author: Memon, Shahbaz
Riedel, Morris
Memon, Riedel,
Koeritz, Chris
Grimshaw, Andrew
Neukirchen, Helmut   orcid.org/0000-0001-8595-3748
Date: 2016-05-01
Language: English
Scope: 115-128
University/Institute: Háskóli Íslands
University of Iceland
School: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Department: Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild (HÍ)
Faculty of Industrial and Mechanical Engineering and Computer Science (UI)
Series: Scalable Computing: Practice and Experience;17(2)
ISSN: 1895-1767
DOI: 10.12694/scpe.v17i2.1160
Subject: Statistical data mining; Data processing,; Distributed file system; Gagnavinnsla; Gagnanám; Skráning gagna
URI: https://hdl.handle.net/20.500.11815/184

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Citation:

Shahbaz Memon, Morris Riedel, Shiraz Memon, Chris Koeritz, Andrew Grimshaw, Helmut Neukirchen. (2016). Enabling Scalable Data Processing and Management through Standards-based Job Execution and the Global Federated File System. Scalable Computing: Practice and Experience, 17(2). 115-128. DOI: http://dx.doi.org/10.1051/kmae/2011046

Abstract:

Emerging challenges for scientific communities are to efficiently process big data obtained by experimentation and computational simulations. Supercomputing architectures are available to support scalable and high performant processing environment, but many of the existing algorithm implementations are still unable to cope with its architectural complexity. One approach is to have innovative technologies that effectively use these resources and also deal with geographically dispersed large datasets. Those technologies should be accessible in a way that data scientists who are running data intensive computations do not have to deal with technical intricacies of the underling execution system. Our work primarily focuses on providing data scientists with transparent access to these resources in order to easily analyze data. Impact of our work is given by describing how we enabled access to multiple high performance computing resources through an open standards-based middleware that takes advantage of a unified data management provided by the the Global Federated File System. Our architectural design and its associated implementation is validated by a usecase that requires massivley parallel DBSCAN outlier detection on a 3D point clouds dataset.

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Open Access

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