Data mining on vast data sets as a cluster system benchmark. (20th May 2015)

• Record Type: Journal Article
• Authors: Heinecke, Alexander; Karlstetter, Roman; Pflüger, Dirk; Bungartz, Hans‐Joachim
• Other Names: Olabarriaga Silvia Delgado guestEditor.; Wilkins‐Diehr Nancy guestEditor.; Smari Waleed W. guestEditor.; Bakhouya Mohamed guestEditor.; Fiore Sandro guestEditor.; Aloisio Giovanni guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 7(2016)
• Page Start: 2145
• Page End: 2165
• Publication Date: 2015-05-20
• Subjects: data mining -- CPU/GPU architectures -- platform comparison -- Intel Xeon Phi -- NVIDIA Kepler; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3514
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Comparing different (accelerated) cluster architectures by a single application is a tough piece of work because this application has to be optimized with respect to platform‐dependent features. In this work, we demonstrate such an optimization for a data mining algorithm which solves regression and classification problems on vast data sets. Our technique is based on least squares regression, and its major component is the iterative matrix‐free solution of a linear system of equations. By processing data sets ranging from several hundreds of thousands instances to multi‐million data points in strong‐scaling and weak‐scaling settings, we are able to estimate the amount of parallelism needed to unleash the performance of classic CPU‐based machines and clusters employing Intel Xeon Phi coprocessors and NVIDIA Kepler GPUs. Only in strong‐scaling experiments, GPUs and coprocessors suffer from their tremendous amount of needed parallelism and get outperformed by dual socket Intel Sandy Bridge nodes at large scale (more than 64 nodes/accelerators). However, in weak‐scaling scenarios, a speed‐up larger than 2X over an entire CPU node can be achieved by a single accelerator. Copyright © 2015 John Wiley & Sons, Ltd.