Exascale Workload Characterization and Architecture Implications

Abstract

We use a hybrid methodology based on binary instrumentation and performance counters to characterize a set of proxy applications (mini-apps and PETSc applications) representative of a broad range of scientific applications (and particularly DOE's future high performance computing workloads). From this empirical basis, we create statistical models that extrapolate application properties (instruction mix, memory size, and memory bandwidth) as a function of problem size. We validate them and project the first quantitative characterization of an exascale computing workload. Finally, the exascale workload is used to evaluate a radical new exascale architecture, stacked DRAM with processor under memory (PUM). Of the two projections, one shows major potential benefits in using PUM. However, the second, more conservative projection suggests that only a small number of exascale applications are likely to be memory-bandwidth limited, but even these are fundamentally memory-capacity limited.