Automated code acceleration targeting heterogeneous openCL devices

Abstract

Accelerators can offer exceptional performance advantages. However, programmers need to spend considerable efforts on acceleration, without knowing how sustainable the employed programming models, languages and tools are. To tackle this challenge, we propose and demonstrate a new runtime system called HTrOP that is able to automatically generate and execute OpenCL code from sequential CPU code. HTrOP transforms suitable data-parallel loops into independent OpenCL-typical work-items and handles concrete calls to these devices through a mix of library components and application-specific OpenCL host code. Computational hotspots are identified and can be offloaded to different resources (CPU, GPGPU and Xeon Phi). We demonstrate the potential of HTrOP on a broad set of applications and are able to improve the performance by 4.3X on average.