Abstract
Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory tiering, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. Using this framework, this study evaluates and compares the impact of a variety of data tiering choices, including how the system prioritizes objects for faster memory as well as the frequency and timing of migration events. The results, collected on a modern Intel platform with conventional DRAM as well as non-volatile RAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared to standard hardware- and software-directed data tiering strategies.
Original language | English |
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Title of host publication | ISMM 2023 - Proceedings of the 2023 ACM SIGPLAN International Symposium on Memory Management, Co-located with PLDI 2023 |
Editors | Stephen M. Blackburn, Erez Petrank |
Publisher | Association for Computing Machinery |
Pages | 163-175 |
Number of pages | 13 |
ISBN (Electronic) | 9798400701795 |
DOIs | |
State | Published - Jun 6 2023 |
Event | 2023 ACM SIGPLAN International Symposium on Memory Management, ISMM 2023 - Orlando, United States Duration: Jun 18 2023 → Jun 18 2023 |
Publication series
Name | International Symposium on Memory Management, ISMM |
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Conference
Conference | 2023 ACM SIGPLAN International Symposium on Memory Management, ISMM 2023 |
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Country/Territory | United States |
City | Orlando |
Period | 06/18/23 → 06/18/23 |
Funding
We thank the anonymous reviewers for their thoughtful comments and feedback. This research was supported by the U.S. DOE Exascale Computing Project (17-SC-20-SC) and the National Science Foundation under CNS-1943305.
Keywords
- NVM
- heterogeneous memory systems
- memory management
- profiling
- runtime systems