Data mining on vast data sets as a cluster system benchmark
Alexander Heinecke, Roman Karlstetter, Dirk Pflüger, Hans‐Joachim Bungartz · Concurrency and Computation Practice and Experience · 2015
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.