OpenTuner can help users achieve better or more portable performance in their speci c domain through program autotuning. A key challenge for users seeking good autotuning performance in OpenTuner is selecting a search approach appropriate for problem. However, not only are current in-situ learning search approaches not robust enough to handle all search spaces, but there are also too many possi...

The process of empirical autotuning results in the generation of many code variants which are tested, found to be suboptimal, and discarded. By retaining annotated performance profiles of each variant tested over the course of many autotuning runs of the same code across different hardware environments and different input datasets, we can apply machine learning algorithms to generate classifier...

OpenCL standard reaches more wider audience due to increasing the number of devices supporting it. This situation puts developers who want performance on large range of platforms in a difficult position. To solve this problem, autotuning frameworks are deployed. But the problem of design exploration space is seriously large because of OpenCL parameters. In this work, we introduce an approach wh...

Autotuning methods are typically conceived as procedures, thus need simulating as digital blocks. However, when no autotuning is in progress, it is far more efficient to represent the tuned controller as a continuous-time system, to exploit variable-step integration. This manuscript presents the first nucleus of a Modelica library of autotuning controllers, where the problem just mentioned is t...

In this paper we demonstrate that cyclotrons can be made to have precisely constant betatron tunes over wide energy ranges. particular, show the horizontal tune and does not follow Lorentz factor gamma, while still perfectly satisfying isochronous condition. To make demonstration developed a technique based on calculation of entirely from geometry realistic non-hard-edge closed orbits. We prese...