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Proposal of Parallel Processing Area Extraction and Data Transfer Number Reduction for Automatic GPU Offloading of IoT Applications

  • Yoji Yamato
  • Hirofumi Noguchi
  • Misao Kataoka
  • Takuma Isoda
  • Tatsuya Demizu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)

Abstract

Recently, IoT (Internet of Things) technologies have been progressed. To overcome of the high cost of developing IoT services by vertically integrating devices and services, Open IoT enables various IoT services to be developed by integrating horizontally separated devices and services. For Open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically and an automatic GPU (graphics processing unit) offloading technology as an elementary technology of Tacit Computing. However, it can improve limited applications because it only optimizes parallelizable loop statements extraction. Therefore, in this paper, to improve performances of more applications automatically, we propose an improved method with reduction of data transfer between CPU and GPU. This can improve performance of many IoT applications. We evaluate our proposed GPU offloading method by applying it to Darknet which is general large application for CPU and find that it can process it 3 times as quickly as only using CPUs within 10 h tuning time.

Keywords

Open IoT GPGPU Automatic offloading 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.NTT Network Service Systems LaboratoriesNTT CorporationTokyoJapan

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