A GPU-Enabled Mobile Telemedicine Training System for Graphic Rendering

被引:1
|
作者
Fu, Zhipeng [1 ]
Zhou, Jun [1 ]
Xu, Wanpeng [1 ,2 ]
机构
[1] Peng Cheng Lab, Shenzhen, Peoples R China
[2] Space Engn Univ, Postgrad Sch, Beijing, Peoples R China
关键词
Telemedicine; Graphic Rendering; Mobile Device; Topological Structure; GPU;
D O I
10.1145/3495243.3558269
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Aiming for overcoming the constraints in graphic rendering of mobile devices used in telemedicine training for 3D images, this paper presents a GPU-enabled mobile telemedicine training system for graphic rendering. Two improvements are made to seamlessly display interactive 3D images of human organs, bones and blood vessels with real-time rendering. Firstly, instead of multiple stages of instruction translation between OpenGL ES and OpenGL, a bespoke GPU driver is added in Virtual Android to invoke GPU resources directly. Secondly, a Video Process Unit (VPU) is added to the hardware layer replacing CPU to code rendered results in H.264 format, reducing CPU load significantly. Test results suggest that the system can deliver consistent performance even in mobile devices of weak capability and a single server can support up to 24 concurrent virtual Android operating systems, each of which connects to 5 clients. The framework proposed by this paper is not only suitable for telemedicine training, but also for other application areas such as Virtual Reality and Augmented Reality in mobile environment.
引用
收藏
页码:877 / 879
页数:3
相关论文
共 50 条
  • [41] GPU-ENABLED HIGH-FIDELITY LES SIMULATIONS FOR TURBOMACHINERY FLOWS
    Osusky, Michal
    Bhaskaran, Rathakrishnan
    Kapilavai, Dheeraj
    Sluyter, Greg
    Shankaran, Sriram
    PROCEEDINGS OF ASME TURBO EXPO 2021: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, VOL 2C, 2021,
  • [42] Infrastructure-level Support for GPU-Enabled Deep Learning in DATAVIEW
    Liu, Junwen
    Xiao, Ziyun
    Lu, Shiyong
    Che, Dunren
    Dong, Ming
    Bai, Changxin
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2023, 141 : 723 - 737
  • [43] mlGeNN: accelerating SNN inference using GPU-enabled neural networks
    Turner, James Paul
    Knight, James C.
    Subramanian, Ajay
    Nowotny, Thomas
    NEUROMORPHIC COMPUTING AND ENGINEERING, 2022, 2 (02):
  • [44] CLUS_GPU-BLASTP: accelerated protein sequence alignment using GPU-enabled cluster
    Sita Rani
    O. P. Gupta
    The Journal of Supercomputing, 2017, 73 : 4580 - 4595
  • [45] Cloud-Native GPU-Enabled Architecture for Parallel Video Encoding
    Salcedo-Navarro, Andoni
    Pena-Ortiz, Raul
    Claver, Jose M.
    Garcia-Pineda, Miguel
    Gutierrez-Aguado, Juan
    EURO-PAR 2024: PARALLEL PROCESSING, PT III, EURO-PAR 2024, 2024, 14803 : 327 - 341
  • [46] Algorithms of GPU-enabled reactive force field (ReaxFF) molecular dynamics
    Zheng, Mo
    Li, Xiaoxia
    Guo, Li
    JOURNAL OF MOLECULAR GRAPHICS & MODELLING, 2013, 41 : 1 - 11
  • [47] GPU-enabled High Performance Online Visual Search with High Accuracy
    Cevahir, Ali
    Torii, Junji
    2012 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2012, : 413 - 420
  • [48] GPU-Enabled Asynchronous Multi-level Checkpoint Caching and Prefetching
    Maurya, Avinash
    Rafique, M. Mustafa
    Tonellot, Thierry
    AlSalem, Hussain J.
    Cappello, Franck
    Nicolae, Bogdan
    PROCEEDINGS OF THE 32ND INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE PARALLEL AND DISTRIBUTED COMPUTING, HPDC 2023, 2023, : 73 - 85
  • [49] ONE BILLION AUDIO SOUNDS FROM GPU-ENABLED MODULAR SYNTHESIS
    Turian, Joseph
    Shier, Jordie
    Tzanetakis, George
    McNally, Kirk
    Henry, Max
    2021 24TH INTERNATIONAL CONFERENCE ON DIGITAL AUDIO EFFECTS (DAFX), 2021, : 222 - 229
  • [50] A GPU-enabled acceleration algorithm for the CAM5 cloud microphysics scheme
    Hong, Yan
    Wang, Yuzhu
    Zhang, Xuanying
    Wang, Xiaocong
    Zhang, He
    Jiang, Jinrong
    JOURNAL OF SUPERCOMPUTING, 2023, 79 (16): : 17784 - 17809