 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.
<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.
 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.