YaskSite: Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures

Abstract

The landscape of multi-core architectures is growing more complex and diverse. Optimal application performance tuning parameters can vary widely across CPUs, and finding them in a possibly multidimensional parameter search space can be time consuming, expensive and potentially infeasible. In this work, we introduce YaskSite, a tool capable of tackling these challenges for stencil computations. YaskSite is built upon Intel's YASK framework. It combines YASK's flexibility to deal with different target architectures with the Execution-Cache-Memory performance model, which enables identifying optimal performance parameters analytically without the need to run the code. Further we show that YaskSite's features can be exploited by external tuning frameworks to reliably select the most efficient kernel(s) for the application at hand. To demonstrate this, we integrate YaskSite into Offsite, an offline tuner for explicit ordinary differential equation methods, and show that the generated performance predictions are reliable and accurate, leading to considerable performance gains at minimal code generation time and autotuning costs on the latest Intel Cascade Lake and AMD Rome CPUs.