Evaluation of Automatic GPU and FPGA Offloading for Function Blocks of Applications

In the recent years, systems using FPGAs, GPUs have increased due to their advantages such as power efficiency compared to CPUs. However, use in systems such as FPGAs and GPUs requires understanding hardware-specific technical specifications such as HDL and CUDA, which is a high hurdle. Based on this background, I previously proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code according to the hardware to be placed. As an element of the concept, I proposed a method to automatically offload loop statements of application source code for CPU to FPGA and GPU. In this paper, I propose and evaluate a method for offloading a function block, which is a larger unit, instead of individual loop statements in an application, to achieve higher speed by automatic offloading to GPU and FPGA. I implement the proposed method and evaluate with existing applications offloading to GPU.