An architectural characterization study of data mining and bioinformatics workloads

Data mining is the process of automatically finding implicit, previously unknown, and potentially useful information from large volumes of data. Recent advances in data extraction techniques have resulted in tremendous increase in the input data size of data mining applications. Data mining systems, on the other hand, have been unable to maintain the same rate of growth. Therefore, there is an increasing need to understand the bottlenecks associated with the execution of these applications in modern architectures. In this paper, we present MineBench, a publicly available benchmark suite containing fifteen representative data mining applications belonging to various categories: classification, clustering, association rule mining and optimization. First, we highlight the uniqueness of data mining applications. Subsequently, we evaluate the MineBench applications on an 8-way shared memory (SMP) machine and analyze important performance characteristics such as L1 and L2 cache miss rates, branch misprediction rates.