Manolis Katsaragakis, Christos Baloukas, Lazaros Papadopoulos, Verena Kantere, F. Catthoor, D. Soudris
{"title":"Energy Consumption Evaluation of Optane DC Persistent Memory for Indexing Data Structures","authors":"Manolis Katsaragakis, Christos Baloukas, Lazaros Papadopoulos, Verena Kantere, F. Catthoor, D. Soudris","doi":"10.1109/HiPC56025.2022.00022","DOIUrl":null,"url":null,"abstract":"The Intel Optane DC Persistent Memory (DCPM) is an attractive novel technology for building storage systems for data intensive HPC applications, as it provides lower cost per byte, low standby power and larger capacities than DRAM, with comparable latency. This work provides an in-depth evaluation of the energy consumption of the Optane DCPM, using well-established indexes specifically designed to address the challenges and constraints of the persistent memories. We study the energy efficiency of the Optane DCPM for several indexing data structures and for the LevelDB key-value store, under different types of YCSB workloads. By integrating an Optane DCPM in a memory system, the energy drops by 71.2% and the throughput increases by 37.3% for the LevelDB experiments, compared to a typical SSD storage solution.","PeriodicalId":119363,"journal":{"name":"2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HiPC56025.2022.00022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The Intel Optane DC Persistent Memory (DCPM) is an attractive novel technology for building storage systems for data intensive HPC applications, as it provides lower cost per byte, low standby power and larger capacities than DRAM, with comparable latency. This work provides an in-depth evaluation of the energy consumption of the Optane DCPM, using well-established indexes specifically designed to address the challenges and constraints of the persistent memories. We study the energy efficiency of the Optane DCPM for several indexing data structures and for the LevelDB key-value store, under different types of YCSB workloads. By integrating an Optane DCPM in a memory system, the energy drops by 71.2% and the throughput increases by 37.3% for the LevelDB experiments, compared to a typical SSD storage solution.