Rozdział w tomie pokonferencyjnym
Miniatura
Pliki
Big_Data_Analytics_in_Java.pdf673.75 KB
Licencja
ClosedAccessDostęp zamknięty
 

Big Data Analytics in Java with PCJ Library: Performance Comparison with Hadoop

Uproszczony widok
dc.abstract.enThe focus of this article is to present Big Data analytics using Java and PCJ library. The PCJ library is an award-winning library for development of parallel codes using PGAS programming paradigm. The PCJ can be used for easy implementation of the different algorithms, including ones used for Big Data processing. In this paper, we present performance results for standard benchmarks covering different types of applications from computational intensive, through traditional map-reduce up to communication intensive. The performance is compared to one achieved on the same hardware but using Hadoop. The PCJ implementation has been used with both local file system and HDFS. The code written with the PCJ can be developed much faster as it requires a smaller number of libraries used. Our results show that applications developed with the PCJ library are much faster compare to Hadoop implementation.
dc.affiliationUniwersytet Warszawski
dc.affiliation.departmentInterdyscyplinarne Centrum Modelowania Matematycznego i Komputerowego
dc.conference12th International Conference on Parallel Processing and Applied Mathematics
dc.conference.countryPolska
dc.conference.coverageinternational
dc.conference.datestart2017
dc.conference.placeLublin
dc.conference.shortcutPPAM 2017
dc.contributor.authorNowicki, Marek
dc.contributor.authorRyczkowska, Magdalena
dc.contributor.authorGórski, Łukasz
dc.contributor.authorBała, Piotr
dc.date.accessioned2024-01-24T18:36:16Z
dc.date.available2024-01-24T18:36:16Z
dc.date.issued2018-03-23
dc.description.financeNie dotyczy
dc.description.seriesLecture Notes in Computer Science
dc.identifier.doi10.1007/978-3-319-78054-2_30
dc.identifier.isbn978-3-319-78053-5
dc.identifier.seriesissn0302-9743
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/102287
dc.identifier.weblinkhttps://link.springer.com/chapter/10.1007%2F978-3-319-78054-2_30
dc.languageen
dc.pbn.affiliationcomputer and information sciences
dc.publisher.ministerialSpringer
dc.relation.bookParallel Processing and Applied Mathematics: 12th International Conference, PPAM 2017, Lublin, Poland, September 10-13, 2017, Revised Selected Papers, Part II / ed. Roman Wyrzykowski, Jack Dongarra, Ewa Deelman, Konrad Karczewski
dc.relation.pages318-327
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.subject.enBig Data
dc.subject.enJava
dc.subject.enParallel computing
dc.subject.enHadoop
dc.titleBig Data Analytics in Java with PCJ Library: Performance Comparison with Hadoop
dc.typeMonographChapterConference
dspace.entity.typePublication