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BenchCouncil: International Open Benchmarking Council


BenchCouncil Benchmarking Principles, Methodology, and Procedures

Benchmarking projects

There are several active projects, which are open to everyone who would like to contribute.

BigDataBench: a Scalable Big Data Benchmark Suite

The current version BigDataBench 5.0 provides 13 representative real-world data sets and 25 benchmarks. The benchmarks cover six workload types including online services, offline analytics, graph analytics, data warehouse, NoSQL, and streaming from three important application domains, Internet services (including search engines, social networks, e-commerce), recognition sciences, and medical sciences. Our benchmark suite includes micro benchmarks, each of which is a single data motif, components benchmarks, which consist of the data motif combinations, and end-to-end application benchmarks, which are the combinations of component benchmarks. Meanwhile, data sets have great impacts on workloads behaviors and running performance (CGO’18). Hence, data varieties are considered with the whole spectrum of data types including structured, semi-structured, and unstructured data. Currently, the included data sources are text, graph, table, and image data. Using real data sets as the seed, the data generators—BDGS— generate synthetic data by scaling the seed data while keeping the data characteristics of raw data.

AIBench: a Scalable AI Benchmark Suite

The current version of AIBench 1.0 includes DC AIBench, HPC AI500, AIOT Bench and Edge AIBench. Datacenter AI benchmarks---DC AIBench provides 15 representative data sets and 27 benchmarks for datacenter AI. The benchmarks cover 15 problem domains including image classification, image generation, text-to-text translation, image-to-text, image-to-image, speech-to-text, face embedding, 3D face recognition, object detection, video prediction, image compression, recommendation, 3D object reconstruction, text summarization, and spatial transformer. It consists of 10 micro benchmarks, 15 component benchmarks, and 2 end-to-end application benchmarks: DCMix---a datacenter AI application combination mixed with AI workloads, and E-commerce AI---an end-to-end business AI benchmark. The benchmarks are implemented not only based on main-stream deep learning frameworks like TensorFlow and PyTorch, but also based on traditional programming model like Pthreads, to conduct an apple-to-apple comparison.

Benchmarking for Mobile and Embedded device Intelligence---AIOT Bench provides 3 representative real-world data sets and 12 benchmarks. The benchmarks cover 3 application domains including image recognition, speech recognition and natural language processing. It consists of 9 micro benchmarks and 3 component benchmarks. It covers different platforms, including Android devices and Raspberry Pi. It covers different development tools, including TensorFlow and Caffe2.

Comprehensive End-to-end Edge Computing Benchmarking---Edge AIBench provides 5 representative real-world data sets and 16 benchmarks. The benchmarks cover 4 application scenarios including ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle. It consists of 8 micro benchmarks and 8 component benchmarks. Moreover, it provides an edge computing AI testbed combined with federated learning.

A Benchmark Suite for HPC AI Systems--- HPC AI500 provides 3 representative scientific data sets and 7 benchmarks. The benchmarks cover 3 workload types including extreme weather analysis, high energy physics, and cosmology. It consists of 3 micro benchmarks and 4 component benchmarks. Micro Benchmarks use two software stacks including CUDA and MKL. Component Benchmarks use two software stacks including TensorFlow and Pytorch.

Two end-to-end Big Data and AI application Benchmarks


E-Commerce Big Data and AI

A Benchmark Suite for Medical AI

A Benchmark Suite for Smart Grid


BigDataBench and AIBench Tutorial

Other Benchmarking Proposals

BenchCouncil conferences are open to everyone who would like to contribute benchmarking proposals at any time.

We have received 8 benchmarking proposals.