Boosting Data Center Performance via Intelligently Managed Multi-backend Disaggregated Memory
Published in The International Conference for High Performance Computing, Networking, Storage, and Analysis(SC24), 2024
Jing Wang, Hanzhang Yang, Chao Li, Yiming Zhuansun, Wang Yuan, Cheng Xu, Xiaofeng Hou, Minyi Guo, Yang Hu, Yaqian Zhao
Abstract: Existing disaggregated memory (DM) systems face a problem of underutilized far memory bandwidth, which greatly limits the data throughput when processing data-intensive applications. Specifically, prior works all target runtime design for a single PCIe-based secondary memory device (i.e., single-backend far memory) with low data bandwidth and high system overhead. In this work, we take the first step to realize a well-crafted, multi-backend DM system with scale-out far memory paths. We propose xDM, a novel DM management scheme that can dynamically build and implicitly select appropriate far memory access paths. As part of xDM, we devise a smart far memory configuration strategy that can further optimize bandwidth usage effectiveness by tuning a wide set of key parameters based on synthesized information of application page data. Our design shows up to 3.9x data swap performance speedup, 2.8x data throughput increase, and 5.1x data center task throughput improvement compared with state-of-the-art works.