Study and evaluation of automatic GPU offloading method from various language applications. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Yamato, Yoji
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 37:Issue 1(2022)
• Page Start: 22
• Page End: 39
• Publication Date: 2022-01-02
• Subjects: Environment adaptive software -- GPGPU -- automatic offloading -- various languages -- performance; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2021.1971666
• Languages: English
• ISSNs: 1744-5760

Abstract

ABSTRACT: Heterogeneous hardware other than a small-core central processing unit (CPU) is increasingly being used, such as a graphics processing unit (GPU), field-programmable gate array (FPGA) or many-core CPU. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilise OpenMP, CUDA, and OpenCL. On the basis of this, we previously 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. However, the source language for offloading was mainly C/C++ language applications, and there was no research into common offloading for various language applications. In this paper, for a new challenge, we study a common method for automatically offloading various language applications in not only C language but also Python and Java. We evaluate the effectiveness of the proposed method in multiple applications of various languages. GRAPHICAL ABSTRACT: UF0001