The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approachto modelling and characterizing big
data and AI workloads. We consider each big data and AI workload as a pipeline of one or
more classes of units of computation performed on different initial or intermediate data
inputs. Each class of unit of computation captures the common requirements while being
reasonably divorced from individual implementations, and hence we call it a data motif. For …

The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approach to characterizing big data and AI
workloads. We consider each big data and AI workload as a pipeline of one or more classes
of unit of computations performed on different initial or intermediate data inputs. Each class
of unit of computation captures the common requirements while being reasonably divorced
from individual implementations, and hence we call it a data dwarf. For the first time, among …