Design space pruning and computational workload splitting for autotuning OpenCL applications

Recently, OpenCL standard reached much wider audiences due to the increasing number of devices supporting it. At the same time, we have observed an increase of differences among devices that support OpenCL. This situation offers to developers, who want to get high performance, a large spectrum of platforms. Given the additional OpenCL platform parameters alongside application specific parameters, the design space for exploration is seriously large. Furthermore, availability of more than one kind of device allows distribution of computation on the heterogeneous platform. Automatic design space exploration frameworks are one of the recent approaches to address these problems and to reduce the burden of programmers. In this work, we present our automatic and efficient technique to prune the design space before moving on to the exploration phase and we propose a new method for splitting the computational tasks to computing devices on heterogeneous platforms.