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Heterogeneous hardware other than a small-core central processing unit (CPU) is increasingly being used, such as a graphics processing unit (GPU), field-programmable gate array (FPGA) or many-core CPU. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilise OpenMP, CUDA, and OpenCL. On the basis of this, we previously proposed environment-adaptive software that enables automatic conversion, configuration, and high performance operation of once-written code, in accordance with the hardware to be placed. However, the source language for offloading was mainly C/C++ language applications, and there was no research into common offloading for various language applications. In this paper, for a new challenge, we study a common method for automatically offloading various language applications in not only C language but also Python and Java. We evaluate the effectiveness of the proposed method in multiple applications of various languages.

Study and evaluation of automatic GPU offloading method from various language applications