Techniques for Automated Performance Analysis

TL;DR: This work presents a formulation of scientific codes, nodes, and clusters that reduces complex performance analysis to well-known mathematical techniques and takes a step towards exascale computing.

Abstract: The performance of a particular HPC code depends on a multitude of variables, including compiler selection, optimization flags, OpenMP pool size, file system load, memory usage, MPI configuration, etc. As a result of this complexity, current predictive models have limited applicability, especially at scale. We present a formulation of scientific codes, nodes, and clusters that reduces complex performance analysis to well-known mathematical techniques. Building accurate predictive models and enhancing our understanding of scientific codes at scale is an important step towards exascale computing.