Offsite Autotuning Approach -- Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods

Abstract

Autotuning (AT) is a promising concept to minimize the often tedious manual effort of optimizing scientific application for a specific target platform. Ideally, an AT approach can reliably identify the most efficient implementation variant(s) for a new platform or new characteristics of the input by applying suitable program transformations and analytic models. In this work, we introduce Offsite, an offline AT approach which automates this selection process at installation time by rating implementation variants based on an analytic performance model without requiring time-consuming runtime tests. From abstract multilevel YAML description languages, Offsite automatically derives optimized, platformspecific and problem-specific code of possible variants and applies the performance model to these variants. We apply Offsite to parallel numerical methods for ordinary differential equations (ODEs). In particular, we investigate tuning a specific class of explicit ODE solvers (PIRK methods) for four different initial value problems (IVPs) on three different shared-memory systems. Our experiments demonstrate that Offsite can reliably identify the set of most efficient implementation variants for different given test configurations (ODE solver, IVP, platform) and effectively handle important AT scenarios.