Mapping Estimator for OpenCL Heterogeneous Accelerators

Abstract

To increase computing performance while keeping energy consumption to an acceptable budget, heterogeneous systems are currently investigated. By using dedicated compute units as accelerators to speedup specific parts of an application, hardware resources are better utilised resulting in a more energy efficient computing system. However, the task of performing such application mapping to accelerators is still a challenge, requiring knowledge beyond software domain in order to understand which part of the code fits better to the capability of the hardware available. Currently, there are tools supporting unified frontends and languages to simplify the programming of such heterogeneous systems, however there is still a high dependency of the user to manually perform the final mapping process. This work exposes a machine learning framework used to automatically infer the most suitable accelerator (between FPGA and GPU) for a given code by statically estimating energy efficiency. This framework can be used to assist the developer in deciding the best mapping for its application with an average hit-rate of 85 percent.