Study and evaluation of automatic offloading method in mixed offloading destination environment

Abstract

Heterogeneous hardware other than a small-core central processing unit (CPU) such as a graphics processing unit (GPU), field-programmable gate array (FPGA), or multi-core CPU is increasingly being used. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilize OpenMP, CUDA, and OpenCL. On the basis of this, we have proposed an 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, no techniques have been developed to properly and automatically offload applications in the mixed offloading destination environment such as GPU, FPGA, and multi-core CPU. In this paper, for a new element of environment-adaptive software, we study a method for offloading applications properly and automatically in an environment where the offloading destination is a mix of GPU, FPGA, and multi-core CPU. We evaluate the effectiveness of the proposed method in multiple applications.

Keywords

• Environment-adaptive software
• GPGPU
• FPGA
• automatic offloading
• evolutionary computation
• mixed offloading destination environment