In Proc. of the IEEE Cluster Conference (CLUSTER), Honolulu, HI, September 2017.
Yang Li† Saugata Ghose† Jongmoo Choi‡ Jin Sun† Hui Wang* Onur Mutluu^†
† Carnegie Mellon University
‡ Dankook University
* Beihang University
^ ETH Zürich
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., di erent 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 di fferent advantages of the multiple memory types in a cost-eff ective manner while avoiding the disadvantages of each technology. Memory pages are placed in and migrated between the di fferent 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 a ffect 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 diff erent 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 di erent type of memory, by comprehensively considering access frequency, row buff er 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 e ffectiveness 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, UHMEM 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.
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