Large-scale in-memory analytics on Intel ® Optane™ DC persistent memory
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3399666.3399933.pdf
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Author(s)
Shanbhag, Anil Atmanand
Tatbul Bitim, Emine Nesime
Cohen, David
Madden, Samuel R
Date Issued
June 2020
Journal
DaMoN '20: Proceedings of the 16th International Workshop on Data Management on New Hardware
Publisher
Association for Computing Machinery (ACM)
Citation
Shanbhag, Anil et al. "Large-scale in-memory analytics on Intel ® Optane™ DC persistent memory." DaMoN '20: Proceedings of the 16th International Workshop on Data Management on New Hardware, June 2020, Portland, Oregon, Association for Computing Machinery, June 2020.
Version
Final published version
Abstract
New data storage technologies such as the recently introduced Intel® Optane™ DC Persistent Memory Module (PMM) offer exciting opportunities for optimizing the query processing performance of database workloads. In particular, the unique combination of low latency, byte-addressability, persistence, and large capacity make persistent memory (PMem) an attractive alternative along with DRAM and SSDs. Exploring the performance characteristics of this new medium is the first critical step in understanding how it will impact the design and performance of database systems. In this paper, we present one of the first experimental studies on characterizing Intel® Optane™ DC PMM's performance behavior in the context of analytical database workloads. First, we analyze basic access patterns common in such workloads, such as sequential, selective, and random reads as well as the complete Star Schema Benchmark, comparing standalone DRAM- and PMem-based implementations. Then we extend our analysis to join algorithms over larger datasets, which require using DRAM and PMem in a hybrid fashion while paying special attention to the read-write asymmetry of PMem. Our study reveals interesting performance tradeoffs that can help guide the design of next-generation OLAP systems in presence of persistent memory in the storage hierarchy.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3399666.3399933
