In recent years, with the slowing down of Moore's law, utilization of hardware other than CPU such as GPU or FPGA is increasing. However, when using heterogeneous hardware other than CPUs, barriers of technical skills such as CUDA and HDL 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 applicapable software and enhance performances more. I evaluate the effectiveness of the proposed method in multiple applications.