Efficient pattern mining on shared memory systems: implications for chip multiprocessor architectures

Abstract

Frequent pattern mining is a fundamental data mining process which has practical applications ranging from market basket data analysis to web link analysis. In this work, we show that state-of-the-art frequent pattern mining applications are inefficient when executing on a shared memory multiprocessor system, due primarily to poor utilization of the memory hierarchy. To improve the efficiency of these applications, we explore memory performance improvements, task partitioning strategies, and task queuing models designed to maximize the scalability of pattern mining on SMP systems. Empirically, we show that the proposed strategies afford significantly improved performance. We also discuss implications of this work in light of recent trends in micro-architecture design, particularly chip multiprocessors (CMPs).