TY - GEN
T1 - Flexible and Effective Object Tiering for Heterogeneous Memory Systems
AU - Kammerdiener, Brandon
AU - Mcmichael, J. Zach
AU - Jantz, Michael R.
AU - Doshi, Kshitij A.
AU - Jones, Terry
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/6/6
Y1 - 2023/6/6
N2 - 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.
AB - 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.
KW - NVM
KW - heterogeneous memory systems
KW - memory management
KW - profiling
KW - runtime systems
UR - http://www.scopus.com/inward/record.url?scp=85163650795&partnerID=8YFLogxK
U2 - 10.1145/3591195.3595277
DO - 10.1145/3591195.3595277
M3 - Conference contribution
AN - SCOPUS:85163650795
T3 - International Symposium on Memory Management, ISMM
SP - 163
EP - 175
BT - ISMM 2023 - Proceedings of the 2023 ACM SIGPLAN International Symposium on Memory Management, Co-located with PLDI 2023
A2 - Blackburn, Stephen M.
A2 - Petrank, Erez
PB - Association for Computing Machinery
T2 - 2023 ACM SIGPLAN International Symposium on Memory Management, ISMM 2023
Y2 - 18 June 2023 through 18 June 2023
ER -