Durable and Energy Efficient In-Memory Frequent-Pattern Mining

Abstract

It is a significant problem to efficiently identify the frequently occurring patterns in a given dataset, so as to unveil the trends hidden behind the dataset. This paper is motivated by the serious demands of a high-performance in-memory frequent-pattern mining strategy, with joint optimization over the mining performance and system durability. While the widely used frequent-pattern tree (FP-tree) serves as an efficient approach for frequent-pattern mining, its construction procedure often makes it unfriendly for nonvolatile memories (NVMs). In particular, the incremental construction of FP-tree could generate many unnecessary writes to the NVM and greatly degrade the energy efficiency, because NVM writes typically take more time and energy than reads. To overcome the drawbacks of FP-tree on NVMs, this paper proposes evergreen FP-tree (EvFP-tree), which includes a lazy counter and a minimum-bit-altered (MBA) encoding scheme to make FP-tree friendly for NVMs. The basic idea of the lazy counter is to greatly eliminate the redundant writes generated in FP-tree construction. On the other hand, the MBA encoding scheme is to complement existing wear-leveling techniques to evenly write each memory cell to extend the NVM lifetime. As verified by experiments, EvFP-tree greatly enhances the mining performance and system lifetime by 40.28% and 87.20% on average, respectively. And EvFP-tree reduces the energy consumption by 50.30% on average.