 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, the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload is not scalable, or even impossible for Big Data and AI benchmarking. Second, it is prohibitively expensive to tailor the architecture to 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 classes of units of computation , which we call dwarfs, among big data and AI workloads.