@inproceedings{xiong2024superbench,
author = {Xiong, Yifan and Jiang, Yuting and Yang, Ziyue and Qu, Lei and Zhao, Guoshuai and Liu, Shuguang and Zhong, Dong and Pinzur, Boris and Zhang, Jie and Wang, Yang and Jose, Jithin and Pourreza, Hossein and Baxter, Jeff and Datta, Kushal and Ram, Prabhat and Melton, Luke and Chau, Joe and Cheng, Peng and Xiong, Yongqiang and Zhou, Lidong},
title = {SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation},
booktitle = {USENIX ATC},
year = {2024},
month = {July},
abstract = {Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, so called "gray failure", for AI workloads, significantly affecting end-to-end performance and concealing performance issues, which complicates root cause analysis for failures and regressions.

We introduce SuperBench, a proactive validation system for AI infrastructure that mitigates hidden degradation caused by hardware redundancies and enhances overall reliability. SuperBench features a comprehensive benchmark suite, capable of evaluating individual hardware components and representing most real AI workloads. It comprises a Validator which learns benchmark criteria to clearly pinpoint defective components. Additionally, SuperBench incorporates a Selector to balance validation time and issue-related penalties, enabling optimal timing for validation execution with a tailored subset of benchmarks. Through testbed evaluation and simulation, we demonstrate that SuperBench can increase the mean time between incidents by up to 22.61x. SuperBench has been successfully deployed in Azure production, validating hundreds of thousands of GPUs over the last two years.},
publisher = {USENIX Association},
url = {http://approjects.co.za/?big=en-us/research/publication/superbench/},
pages = {835-850},
}