Efficient OpenCL-based concurrent tasks offloading on accelerators
Department of Computer Architecture, University of Malaga, Spain
International Conference on Computational Science (ICCS), 2017
@article{lazaro2017efficient,
title={Efficient OpenCL-based concurrent tasks offloading on accelerators},
author={L{‘a}zaro-Mu{~n}oz, AJ and Gonz{‘a}lez-Linares, JM and G{‘o}mez-Luna, J and Guil, N},
journal={Procedia Computer Science},
volume={108},
pages={2353–2357},
year={2017},
publisher={Elsevier}
}
Current heterogeneous platforms with CPUs and accelerators have the ability to launch several independent tasks simultaneously, in order to exploit concurrency among them. These tasks typically consist of data transfer commands and kernel computation commands. In this paper we develop a runtime approach to optimize the concurrency between data transfers and kernel computation commands in a multithreaded scenario where each CPU thread offloads tasks to the accelerator. It deploys a heuristic based on a temporal execution model for concurrent tasks. It is able to establish a near-optimal task execution order that significantly reduces the total execution time, including data transfers. Our approach has been evaluated employing five different benchmarks composed of dominant kernel and dominant transfer real tasks. In these experiments our heuristic achieves speedups up to 1.5x in AMD R9 and NVIDIA K20c accelerators and 1.3x in an Intel Xeon Phi (KNC) device.
June 17, 2017 by hgpu
