混合卸载目标环境下的自动卸载方法提案
Proposal of Automatic Offloading Method in Mixed Offloading Destination Environment
AI总结：
针对异构硬件使用技术门槛高且现有技术无法在GPU、FPGA、众核CPU混合环境下自动合理卸载应用的问题，提出了作为环境自适应软件新要素的混合目标环境自动卸载方法。
摘要：
在使用异构硬件时，OpenMP、CUDA、OpenCL等技术技能门槛较高。基于此，笔者此前提出了可实现自动转换与配置的环境自适应软件。但包括现有技术在内，尚无研究能在GPU、FPGA与众核CPU混合的卸载目标环境中，实现合理且自动化的应用卸载。本文作为环境自适应软件的新组成要素，研究了一种在卸载目标混合了GPU、FPGA与众核CPU的环境中，对应用进行合理自动卸载的方法。
英文摘要：
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."
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
