 First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation , which we call dwarfs, among big data and AI workloads. Second, for the sake of fairness, the benchmarks must include diversity of data and workloads. Third, for co-design of software and hardware, the benchmarks should be consistent across different communities. Other than creating a new benchmark or proxy for every possible workload, we propose using dwarf-based benchmarks--the combination of eight dwarfs--to represent diversity of big data and AI workloads.
<meaning-changed>  First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation , which we call dwarfs, among big data and AI workloads. Second, for the sake of fairness, the benchmarks must include diversity of data and workloads. Third, for co-design of software and hardware, the benchmarks should be consistent across different communities. Other than creating a new benchmark or proxy for every possible workload, we propose using dwarf-based benchmarks--the combination of eight dwarfs--to represent diversity of big data and AI workloads.
 First, for the sake of conciseness, benchmarking scalability, portability cost, reproducibility, and better interpretation of performance data, we need understand what are the abstractions of frequently-appearing units of computation , which we call a data motif. On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.