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Several fundamental changes in technology indicate domain-specific hardwareand software co-design is the only path left. In this context, architecture,system, data management, and machine learning communities pay greater attentionto innovative big data and AI algorithms, architecture, and systems.Unfortunately, complexity, diversity, frequently-changed workloads, and rapidevolution of big data and AI systems raise great challenges. First, thetraditional benchmarking methodology that creates a new benchmark or proxy forevery possible workload is not scalable, or even impossible for Big Data and AIbenchmarking. Second, it is prohibitively expensive to tailor the architectureto characteristics of one or more application or even a domain of applications.We consider each big data and AI workload as a pipeline of one or more classesof units of computation performed on different initial or intermediate datainputs, each class of which we call a data motif. On the basis of our previouswork that identifies eight data motifs taking up most of the run time of a widevariety of big data and AI workloads, we propose a scalable benchmarkingmethodology that uses the combination of one or more data motifs---to representdiversity of big data and AI workloads. Following this methodology, we presenta unified big data and AI benchmark suite---BigDataBench 4.0, publiclyavailable from~\url{http://prof.ict.ac.cn/BigDataBench}. This unified benchmarksuite sheds new light on domain-specific hardware and software co-design:tailoring the system and architecture to characteristics of the unified eightdata motifs other than one or more application case by case. Also, for thefirst time, we comprehensively characterize the CPU pipeline efficiency usingthe benchmarks of seven workload types in BigDataBench 4.0.

BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite