AIBench: An Industry Standard Internet Service AI Benchmark Suite
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Aug 2019.
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Citing papers per year: 2022: 2; 2023: 4.
6 papers.
- 2022XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaversecited by 14
- 2023TorchBench: Benchmarking PyTorch with High API Surface Coveragecited by 2
- 2023Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fieldscited by 1
- 2023SAIBench: A Structural Interpretation of AI for Science Through Benchmarkscited by 1
- 2022Quality at the Tail of Machine Learning Inferencecited by 0
- 2023Enabling Accelerators for Graph Computingcited by 0
- 2023Enabling Accelerators for Graph Computing16 Dec 2023
- 2023SAIBench: A Structural Interpretation of AI for Science Through Benchmarks29 Nov 2023
- 2023Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields11 Aug 2023
- 2023TorchBench: Benchmarking PyTorch with High API Surface Coverage27 Apr 2023
- 2022Quality at the Tail of Machine Learning Inference25 Dec 2022
- 2022XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse16 Nov 2022