Proposal of Automatic Offloading Method in Mixed Offloading Destination Environment

TL;DR

This paper introduces an environment-adaptive software approach that automatically and properly offloads applications across mixed hardware destinations including GPU, FPGA, and many-core CPUs, simplifying heterogeneous computing.

Contribution

It proposes a novel method for automatic offloading in environments with diverse hardware, addressing a gap in existing environment-adaptive software solutions.

Findings

Successfully offloads applications to mixed hardware environments

Improves efficiency of heterogeneous computing tasks

Automates offloading process reducing technical skill barriers

Abstract

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. 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. Y. Yamato, "Proposal of Automatic Offloading Method in Mixed Offloading Destination Environment," 2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW 2020), pp.460-464, DOI: 10.1109/CANDARW51189.2020.00094, Nov. 2020. "(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."