@article{10902/35074,
year = {2019},
month = {3},
url = {https://hdl.handle.net/10902/35074},
abstract = {Heterogeneous systems are the core architecture of most of the high-performance computing nodes, due to their excellent performance and energy efficiency. However, a key challenge that remains is programmability, specifically, releasing the programmer from the burden of managing data and devices with different architectures. To this end, we extend EngineCL to support FPGA devices. Based on OpenCL, EngineCL is a high-level framework providing load balancing among devices. Our proposal fully integrates FPGAs into the framework, enabling effective cooperation between CPU, GPU, and FPGA. With command overlapping and judicious data management, our work improves performance by up to 96% compared with single-device execution and delivers energy-delay gains of up to 37%. In addition, adopting FPGAs does not require programmers to make big changes in their applications because the extensions do not modify the user-facing interface of EngineCL.},
organization = {This work was supported in part by grants TIN2016-76635-C2 (AEI/FEDER, UE), gaZ: T48 research group (Aragón Gov. and European ESF), the University of Zaragoza (JIUZ-2017-TEC-09), HiPEAC4 (European H2020/687698), the Spanish Ministry of Education (FPU16/03299) and the CAPAP-H Network grant TIN2016-81840-REDT. M. A. Dávila-Guzmán is supported by a Universidad de Zaragoza-Banco Santander PhD scholarship.},
publisher = {Kluwer Academic Publishers},
publisher = {Journal of Supercomputing, 2019, 75(3), 1732-1746},
title = {Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL},
author = {Dávila Guzmán, María Angélica and Nozal, Raúl and Gran Tejero, Rubén and Villarroya-Guadó, María and Suárez Gracia, Darío and Bosque Orero, José Luis},
}