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Archivio della ricerca- Università di Roma La Sapienza
Doctoral thesis . 2015
License: http://creativecommons.org/licenses/by-nd/3.0/it/
Full-Text: https://iris.uniroma1.it/bitstream/11573/864567/1/Tesi%20dottorato%20Carbone
Data sources: Archivio della ricerca- Università di Roma La Sapienza
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HPC techniques for large scale data analysis

descriptionPublicationkeyboard_double_arrow_right Doctoral thesis 12 Dec 2015 Italy English Publisher:Università degli Studi di Roma "La Sapienza"
Authors: CARBONE, Giancarlo;

handle: 11573/864567

HPC techniques for large scale data analysis

Abstract

In the present work we apply High-Performance Computing techniques to two Big Data problems. The frst one deals with the analysis of large graphs by using a parallel distributed architecture, whereas the second one consists in the design and implementation of a scalable solution for fast indexing and searching of large datasets of heterogeneous documents.

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Italy
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Keywords

graph algorithms; GPU; high performance computing; large scale data; betweenness centrality; breadth first search; computer forensics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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