TY - GEN
T1 - UPM-DMA
T2 - 21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021
AU - Zhu, Jinbin
AU - Xiao, Limin
AU - Wang, Liang
AU - Qin, Guangjun
AU - Zhang, Rui
AU - Liu, Yuting
AU - Liu, Zhonglin
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Fast storage devices (e.g., NVMe SSDs), which integrate new storage medium and parallel architecture, provide promising storage solutions for cloud data center loads. However, they make the lengthy IO stack that is negligible in the past become a new performance bottleneck. Current user-mode solutions can eliminate context switches between kernel and user space by moving partial kernel IO stack into userspace. Unfortunately, they introduce additional overhead for pinning memory used by DMA in userspace which is quite time-consuming for IO-intensive workloads. To address this issue, we present UPM-DMA, an alternative and efficient memory management strategy for NVMe SSDs to lighten the IO stack by amortizing per-request latency. UPM-DMA can dynamically select the appropriate pinned memory mode for different IO requests, especially the pinned memory pool for medium data IO requests. We further explore different parameter settings to optimize system performance and memory space efficiency. Finally, we implement the overall strategy as a memory library named UPM libs and integrate it into the SPDK framework. The official benchmarks, SPDK perf, are adopted to evaluate our solution. The experimental results show that compared with the default processing method in SPDK, there is an improvement of at least 17% under various test data sizes.
AB - Fast storage devices (e.g., NVMe SSDs), which integrate new storage medium and parallel architecture, provide promising storage solutions for cloud data center loads. However, they make the lengthy IO stack that is negligible in the past become a new performance bottleneck. Current user-mode solutions can eliminate context switches between kernel and user space by moving partial kernel IO stack into userspace. Unfortunately, they introduce additional overhead for pinning memory used by DMA in userspace which is quite time-consuming for IO-intensive workloads. To address this issue, we present UPM-DMA, an alternative and efficient memory management strategy for NVMe SSDs to lighten the IO stack by amortizing per-request latency. UPM-DMA can dynamically select the appropriate pinned memory mode for different IO requests, especially the pinned memory pool for medium data IO requests. We further explore different parameter settings to optimize system performance and memory space efficiency. Finally, we implement the overall strategy as a memory library named UPM libs and integrate it into the SPDK framework. The official benchmarks, SPDK perf, are adopted to evaluate our solution. The experimental results show that compared with the default processing method in SPDK, there is an improvement of at least 17% under various test data sizes.
KW - IO-intensive
KW - NVMe SSDs
KW - Pinned memory
KW - User space
UR - https://www.scopus.com/pages/publications/85126212414
U2 - 10.1007/978-3-030-95384-3_17
DO - 10.1007/978-3-030-95384-3_17
M3 - 会议稿件
AN - SCOPUS:85126212414
SN - 9783030953836
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 257
EP - 270
BT - Algorithms and Architectures for Parallel Processing - 21st International Conference, ICA3PP 2021, Proceedings
A2 - Lai, Yongxuan
A2 - Wang, Tian
A2 - Jiang, Min
A2 - Xu, Guangquan
A2 - Liang, Wei
A2 - Castiglione, Aniello
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 December 2021 through 5 December 2021
ER -