  Autotuning techniques are a promising approach to minimize the otherwise
tedious manual effort of optimizing scientific applications for a specific
target platform. Ideally, an autotuning approach is capable of reliably
identifying the most efficient implementation variant(s) for a new target
system or new characteristics of the input by applying suitable program
transformations and analytic models. In this work, we introduce Offsite, an
offline autotuning approach which automates this selection process at
installation time by rating implementation variants based on an analytic
performance model without requiring time-consuming runtime experiments. From
abstract multilevel YAML description languages, Offsite automatically derives
optimized, platform-specific and problem-specific code of possible
implementation variants and applies the performance model to these
implementation 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 various initial value problems (IVPs)
on shared-memory systems. Our experiments demonstrate that Offsite is able to
reliably identify a set of the most efficient implementation variants for given
test configurations (ODE solver, IVP, platform) and is capable of effectively
handling important autotuning scenarios.
