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Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core Vignesh T. Ravi Gagan Agrawal
 

Summary: Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core
Cluster
Vignesh T. Ravi Gagan Agrawal
Department of Computer Science and Engineering
The Ohio State University Columbus OH 43210
{raviv,agrawal}@cse.ohio-state.edu
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 paralleliza-
tion techniques developed in our run-time system on a clus-
ter of multi-cores, and 2) We report on a detailed performance
study to understand why certain parallelization techniques out-

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University

 

Collections: Computer Technologies and Information Sciences