Collie: Finding Performance Anomalies in RDMA Subsystems


Xinhao Kong, Duke University and ByteDance Inc.; Yibo Zhu, Huaping Zhou, Zhuo Jiang, Jianxi Ye, and Chuanxiong Guo, ByteDance Inc.; Danyang Zhuo, Duke University


High-speed RDMA networks are getting rapidly adopted in the industry for their low latency and reduced CPU overheads. To verify that RDMA can be used in production, system administrators need to understand the set of application workloads that can potentially trigger abnormal performance behaviors (e.g., unexpected low throughput, PFC pause frame storm). We design and implement Collie, a tool for users to systematically uncover performance anomalies in RDMA subsystems without the need to access hardware internal designs. Instead of individually testing each hardware device (e.g., NIC, memory, PCIe), Collie is holistic, constructing a comprehensive search space for application workloads. Collie then uses simulated annealing to drive RDMA-related performance and diagnostic counters to extreme value regions to find workloads that can trigger performance anomalies. We evaluate Collie on combinations of various RDMA NIC, CPU, and other hardware components. Collie found 15 new performance anomalies. All of them are acknowledged by the hardware vendors. 7 of them are already fixed after we reported them. We also present our experience in using Collie to avoid performance anomalies for an RDMA RPC library and an RDMA distributed machine learning framework.

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {278389,
author = {Xinhao Kong and Yibo Zhu and Huaping Zhou and Zhuo Jiang and Jianxi Ye and Chuanxiong Guo and Danyang Zhuo},
title = {Collie: Finding Performance Anomalies in {RDMA} Subsystems},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {287--305},
url = {},
publisher = {USENIX Association},
month = apr

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