Data‐aware tuning of scientific applications with model‐based autotuning

Abstract

Summary Autotuning is a widely accepted method for adapting the execution of scientific applications to the underlying hardware. One method for speeding up the search for the optimal candidate implementation in autotuning, which often is very time‐consuming, is the performance prediction using an analytical model. Using such a model‐based autotuning method, autotuning may be performed at runtime, which enables the application to consider data characteristics for the tuning decision. The article gives an overview on existing works, which apply model‐based autotuning for data‐aware tuning decisions in scientific computing. It shows that model‐based autotuning is a feasible method for increasing the performance of scientific applications by considering influences from the input data, where ‘performance’ may be execution time or energy, among others. It furthermore presents a systematisation of model‐based autotuning: it proposes a distinction of algorithm parameters, machine‐specific parameters and data‐specific parameters. The article gives suggestions on how to set up an application‐specific analytical model, presents an example for that and discusses the limits of model‐based autotuning. Copyright © 2016 John Wiley & Sons, Ltd.