Utility-Based Hybrid Memory Management

Yang Li, Saugata Ghose, Jongmoo Choi, Jin Sun, Hui Wang, Onur Mutlu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


While the memory footprints of cloud and HPC applications continue to increase, fundamental issues with DRAM scaling are likely to prevent traditional main memory systems, composed of monolithic DRAM, from greatly growing in capacity. Hybrid memory systems can mitigate the scaling limitations of monolithic DRAM by pairing together multiple memory technologies (e.g., different types of DRAM, or DRAM and non-volatile memory) at the same level of the memory hierarchy. The goal of a hybrid main memory is to combine the different advantages of the multiple memory types in a cost-effective manner while avoiding the disadvantages of each technology. Memory pages are placed in and migrated between the different memories within a hybrid memory system, based on the properties of each page. It is important to make intelligent page management (i.e., placement and migration) decisions, as they can significantly affect system performance.In this paper, we propose utility-based hybrid memory management (UH-MEM), a new page management mechanism for various hybrid memories, that systematically estimates the utility (i.e., the system performance benefit) of migrating a page between different memory types, and uses this information to guide data placement. UH-MEM operates in two steps. First, it estimates how much a single application would benefit from migrating one of its pages to a different type of memory, by comprehensively considering access frequency, row buffer locality, and memory-level parallelism. Second, it translates the estimated benefit of a single application to an estimate of the overall system performance benefit from such a migration.We evaluate the effectiveness of UH-MEM with various types of hybrid memories, and show that it significantly improves system performance on each of these hybrid memories. For a memory system with DRAM and non-volatile memory, UH-MEM improves performance by 14% on average (and up to 26%) compared to the best of three evaluated state-of-The-Art mechanisms across a large number of data-intensive workloads.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages14
ISBN (Electronic)9781538623268
StatePublished - Sep 22 2017
Externally publishedYes
Event2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 - Honolulu, United States
Duration: Sep 5 2017Sep 8 2017

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)1552-5244


Other2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
Country/TerritoryUnited States


  • Hybrid memory
  • Non-volatile memory
  • Performance modeling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Signal Processing


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