TY - GEN
T1 - Performance Evaluation on CXL-enabled Hybrid Memory Pool
AU - Yang, Qirui
AU - Jin, Runyu
AU - Davis, Bridget
AU - Inupakutika, Devasena
AU - Zhao, Ming
N1 - Funding Information:
Partly supported by National Science Foundation 1955593, 2126291
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The emerging cache coherent Compute Express Link (CXL) interconnect provides a practical way to disaggregate cloud memory resources from monolithic servers into memory pools with DRAM-level access latency. While DRAM-only memory pool improves the resource utilization and reduces the Total Cost of Ownership (TCO) for cloud providers, we investigate the possibility of applying cheaper SSDs to a memory pooling system to further reduce the cost of cloud servers without sacrificing the application's performance. In this study, we build a simulated CXL-enabled DRAM-SSD hybrid memory pool based on Linux and commodity hardware, and conduct performance evaluation by running representative cloud workloads which cover deep learning training, database, data analytics and video processing on the testbed. The evaluation results show that a hybrid memory pool can potentially reduce memory cost while maintaining the same level of application performance for computation-intensive applications. For example, with memory overcommit ratio of 2, the performance degradation of training ResNet50 on ImageNet dataset is only 2.68%.
AB - The emerging cache coherent Compute Express Link (CXL) interconnect provides a practical way to disaggregate cloud memory resources from monolithic servers into memory pools with DRAM-level access latency. While DRAM-only memory pool improves the resource utilization and reduces the Total Cost of Ownership (TCO) for cloud providers, we investigate the possibility of applying cheaper SSDs to a memory pooling system to further reduce the cost of cloud servers without sacrificing the application's performance. In this study, we build a simulated CXL-enabled DRAM-SSD hybrid memory pool based on Linux and commodity hardware, and conduct performance evaluation by running representative cloud workloads which cover deep learning training, database, data analytics and video processing on the testbed. The evaluation results show that a hybrid memory pool can potentially reduce memory cost while maintaining the same level of application performance for computation-intensive applications. For example, with memory overcommit ratio of 2, the performance degradation of training ResNet50 on ImageNet dataset is only 2.68%.
KW - CXL
KW - DRAM
KW - Hybrid Memory Pool
KW - Memory Disaggregation
KW - NUMA
KW - NVMe
KW - SSD
UR - http://www.scopus.com/inward/record.url?scp=85142279772&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142279772&partnerID=8YFLogxK
U2 - 10.1109/NAS55553.2022.9925356
DO - 10.1109/NAS55553.2022.9925356
M3 - Conference contribution
AN - SCOPUS:85142279772
T3 - 2022 IEEE International Conference on Networking, Architecture and Storage, NAS 2022 - Proceedings
BT - 2022 IEEE International Conference on Networking, Architecture and Storage, NAS 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Networking, Architecture and Storage, NAS 2022
Y2 - 3 October 2022 through 4 October 2022
ER -