  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.
