Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

Maria Angelica Davila Guzman, Raul Nozal, Ruben Gran Tejero, Maria Villarroya-Gaudo, Dario Suarez Gracia, Jose Luis Bosque
Universidad de Zaragoza, Spain
The Journal of Supercomputing, 2019


   title={Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL},

   author={Guzm{‘a}n, Mar{‘i}a Ang{‘e}lica D{‘a}vila and Nozal, Ra{‘u}l and Tejero, Rub{‘e}n Gran and Villarroya-Gaud{‘o}, Mar{‘i}a and Gracia, Dar{‘i}o Su{‘a}rez and Bosque, Jose Luis},

   journal={The Journal of Supercomputing},





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.
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