Yi Lin, Po-Chun Huang, Duo Liu, Xiao Zhu, Liang Liang
{"title":"Making In-Memory Frequent Pattern Mining Durable and Energy Efficient","authors":"Yi Lin, Po-Chun Huang, Duo Liu, Xiao Zhu, Liang Liang","doi":"10.1109/ICPP.2016.13","DOIUrl":null,"url":null,"abstract":"It is a significant problem to efficiently identifythe frequently-occurring patterns in a given dataset, so as tounveil the trends hidden behind the dataset. This work ismotivated by the serious demands of a high-performance inmemoryfrequent-pattern mining strategy, with joint optimizationover the mining performance and system durability. While thewidely-used frequent-pattern tree (FP-tree) serves as an efficientapproach for frequent-pattern mining, its construction procedureoften makes it unfriendly for nonvolatile memories (NVMs). Inparticular, the incremental construction of FP-tree could generatemany unnecessary writes to the NVM and greatly degrade theenergy efficiency, because NVM writes typically take more timeand energy than reads. To overcome the drawbacks of FP-treeon 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 basicidea of the lazy counter is to greatly eliminate the redundantwrites generated in FP-tree construction. On the other hand, theMBA encoding scheme is to complement existing wear-levelingtechniques to evenly write each memory cell to extend the NVMlifetime. As verified by experiments, EvFP-tree greatly enhancesthe mining performance and system lifetime by 28.01% and82.10% on average, respectively.","PeriodicalId":409991,"journal":{"name":"2016 45th International Conference on Parallel Processing (ICPP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 45th International Conference on Parallel Processing (ICPP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPP.2016.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
It is a significant problem to efficiently identifythe frequently-occurring patterns in a given dataset, so as tounveil the trends hidden behind the dataset. This work ismotivated by the serious demands of a high-performance inmemoryfrequent-pattern mining strategy, with joint optimizationover the mining performance and system durability. While thewidely-used frequent-pattern tree (FP-tree) serves as an efficientapproach for frequent-pattern mining, its construction procedureoften makes it unfriendly for nonvolatile memories (NVMs). Inparticular, the incremental construction of FP-tree could generatemany unnecessary writes to the NVM and greatly degrade theenergy efficiency, because NVM writes typically take more timeand energy than reads. To overcome the drawbacks of FP-treeon 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 basicidea of the lazy counter is to greatly eliminate the redundantwrites generated in FP-tree construction. On the other hand, theMBA encoding scheme is to complement existing wear-levelingtechniques to evenly write each memory cell to extend the NVMlifetime. As verified by experiments, EvFP-tree greatly enhancesthe mining performance and system lifetime by 28.01% and82.10% on average, respectively.