MineBench: A Benchmark Suite for Data Mining Workloads

TLDR

Data mining is a key scientific and commercial field, yet the rapid growth of data sets and slower computer system improvements have widened the performance gap between data mining algorithms and hardware. This paper introduces MineBench, a publicly available benchmark suite of fifteen representative data mining applications, to analyze algorithmic bottlenecks and help close that performance gap. MineBench comprises fifteen representative data mining applications across clustering, classification, and association rule mining, enabling systematic performance analysis. By understanding algorithmic bottlenecks, computer architectures can be optimized for data mining, and MineBench will aid researchers in characterizing and accelerating these workloads.

Abstract

Data mining constitutes an important class of scientific and commercial applications. Recent advances in data extraction techniques have created vast data sets, which require increasingly complex data mining algorithms to sift through them to generate meaningful information. The disproportionately slower rate of growth of computer systems has led to a sizeable performance gap between data mining systems and algorithms. The first step in closing this gap is to analyze these algorithms and understand their bottlenecks. With this knowledge, current computer architectures can be optimized for data mining applications. In this paper, we present MineBench, a publicly available benchmark suite containing fifteen representative data mining applications belonging to various categories such as clustering, classification, and association rule mining. We believe that MineBench will be of use to those looking to characterize and accelerate data mining workloads