Using Performance Models to Understand Scalable Krylov Solver Performance at Scale for Structured Grid Problems

Abstract

Krylov solvers are key kernels in many large-scale science and engineering applications for solving sparse linear systems. Applications running at scale can experience significant slowdown due to factors such as network congestion, off-node congestion, network distance, and performance variation across processes. Performance models can help us better understand factors limiting performance, however simple models fail to capture slowdowns often occurring at scale and performance variation across multiple runs of the same code. This work develops performance models that capture behavior found at scale and uses these models to guide optimizations for Krylov solvers and related kernels using both blocking and non-blocking communication for structured grid problems at scale. We use detailed performance analysis with network performance counters to show how network behavior relates to observed performance and guide the development of performance models that capture the runtime impact of network congestion, network distance, communication and computation overlap, and process mappings. These models guide us to optimize kernels using MPI protocol changes, node-aware communication, and topology-aware communication. The resulting tools and analysis provide us with a better understanding of how to improve performance at scale that can benefit a wider range of applications.