Title :
Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core Cluster
Author :
Ravi, Vignesh T. ; Agrawal, Gagan
Author_Institution :
Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH
Abstract :
The deluge of available data for analysis demands the need to scale the performance of data mining implementations. With the current architectural trends, one of the major challenges today is achieving programmability and performance for data mining applications on multi-core machines and cluster of multi-core machines. To address this problem, we have been developing a runtime framework, FREERIDE, that enables parallel execution of data mining and data analysis tasks.The contributions of this paper are two-fold: 1) this paper describes and evaluates various shared-memory parallelization techniques developed in our run-time system on a cluster of multi-cores, and 2) we report on a detailed performance study to understand why certain parallelization techniques outperform other techniques for a particular application.
Keywords :
data analysis; data mining; parallel processing; shared memory systems; FREERIDE run-time system framework; data analysis; data mining application; data-intensive application parallelization; multicore machine cluster; performance issue; shared-memory parallelization technique; Application software; Classification tree analysis; Clustering algorithms; Data analysis; Data mining; Frequency; Performance analysis; Runtime; Scalability; Yarn; Data-intensive computing; Middleware; Multi-Core Architectures;
Conference_Titel :
Cluster Computing and the Grid, 2009. CCGRID '09. 9th IEEE/ACM International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3935-5
Electronic_ISBN :
978-0-7695-3622-4
DOI :
10.1109/CCGRID.2009.83
