Proposal and evaluation of adjusting resource amount for automatically offloaded applications

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 many-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, I have 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. Although it has been considered to convert the code in accordance with the offload devices, no method has been developed to set the amount of resources appropriately after automatic conversion. In this paper, as a new element of environment-adapted software, I examine a method of optimizing the amount of resources of the CPU and offload device in order to operate the application with high-cost performance. Through the offloading of multiple existing applications, I demonstrate that the proposed method can meet the user’s request and set an appropriate amount of resources.

Keywords

• Environment adaptive software
• GPGPU
• Automatic Offloading
• evolutionary computation
• Resource Configuration