Before revision:  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.
After revision:  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 performed on different initial or intermediate data inputs, each class of which we call dwarfs, among big data and AI workloads.