Study of Automatic Offloading Method in Mixed Offloading Destination Environment

TL;DR

This paper proposes a novel automatic offloading method for mixed hardware environments, enabling efficient utilization of heterogeneous devices like GPU, FPGA, and many-core CPUs without requiring extensive technical skills.

Contribution

It introduces a new offloading approach for mixed hardware environments, addressing the lack of automatic solutions for heterogeneous offloading destinations.

Findings

Effective offloading demonstrated across multiple applications.

Improved performance in heterogeneous hardware environments.

Automatic conversion and configuration achieved without manual intervention.

Abstract

In recent years, utilization of heterogeneous hardware other than small core CPU such as GPU, FPGA or many core CPU is increasing. However, when using heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and OpenCL 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. However, including existing technologies, there has been no research to properly and automatically offload the mixed offloading destination environment such as GPU, FPGA and many core CPU. In this paper, as a new element of environment-adaptive software, I study a method for offloading applications properly and automatically in the environment where the offloading destination is mixed with GPU, FPGA and many core CPU. I evaluate the effectiveness of the proposed method in multiple applications.