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Improvement Proposal of Automatic GPU Offloading Technology

Recently, utilization of hardware other than CPU (Central Processing Unit) such as GPU (Graphics Processing Unit) or FPGA (Field-Programmable Gate Array) is increasing including education field. However, when using heterogeneous hardware other than CPUs, barriers of technical skills such as CUDA (Compute Unified Device Architecture) and HDL (Hardware Description Language) are high. Based on that, I have proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code, according to the hardware to be placed. Partly of the offloading to the GPU and FPGA was automated previously. In this paper, I improve and propose a previous automatic GPU offloading method to expand applicable software and enhance performances more. I evaluate the effectiveness of the proposed method in multiple applications.