English

AIBench: An Industry Standard Internet Service AI Benchmark Suite

Computer Vision and Pattern Recognition 2019-10-24 v2 Artificial Intelligence Information Retrieval Performance Software Engineering

Abstract

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many innovative AI algorithms, systems, and architectures are proposed, and thus the importance of benchmarking and evaluating them rises. However, modern Internet services adopt a microservice-based architecture and consist of various modules. The diversity of these modules and complexity of execution paths, the massive scale and complex hierarchy of datacenter infrastructure, the confidential issues of data sets and workloads pose great challenges to benchmarking. In this paper, we present the first industry-standard Internet service AI benchmark suite---AIBench with seventeen industry partners, including several top Internet service providers. AIBench provides a highly extensible, configurable, and flexible benchmark framework that contains loosely coupled modules. We identify sixteen prominent AI problem domains like learning to rank, each of which forms an AI component benchmark, from three most important Internet service domains: search engine, social network, and e-commerce, which is by far the most comprehensive AI benchmarking effort. On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales. The specifications, source code, and performance numbers are publicly available from the benchmark council web site http://www.benchcouncil.org/AIBench/index.html.

Keywords

Cite

@article{arxiv.1908.08998,
  title  = {AIBench: An Industry Standard Internet Service AI Benchmark Suite},
  author = {Wanling Gao and Fei Tang and Lei Wang and Jianfeng Zhan and Chunxin Lan and Chunjie Luo and Yunyou Huang and Chen Zheng and Jiahui Dai and Zheng Cao and Daoyi Zheng and Haoning Tang and Kunlin Zhan and Biao Wang and Defei Kong and Tong Wu and Minghe Yu and Chongkang Tan and Huan Li and Xinhui Tian and Yatao Li and Junchao Shao and Zhenyu Wang and Xiaoyu Wang and Hainan Ye},
  journal= {arXiv preprint arXiv:1908.08998},
  year   = {2019}
}

Comments

24 pages

R2 v1 2026-06-23T10:55:32.816Z