Author: Kuhn, A.
Paper Title Page
FRBL05 RemoteVis: An Efficient Library for Remote Visualization of Large Volumes Using NVIDIA Index 1047
  • T.V. Spina, D. Alnajjar, M.L. Bernardi, F.S. Furusato, E.X. Miqueles, A.Z. Peixinho
    LNLS, Campinas, Brazil
  • A. Kuhn, M. Nienhaus
    NVIDIA, Santa Clara, USA
  Funding: We would like to thank the Brazilian Ministry of Science, Technology, and Innovation for the financial support.
Advancements in X-ray detector technology are increasing the amount of volumetric data available for material analysis in synchrotron light sources. Such developments are driving the creation of novel solutions to visualize large datasets both during and after image acquisition. Towards this end, we have devised a library called RemoteVis to visualize large volumes remotely in HPC nodes, using NVIDIA IndeX as the rendering backend. RemoteVis relies on RDMA-based data transfer to move large volumes from local HPC servers, possibly connected to X-ray detectors, to remote dedicated nodes containing multiple GPUs for distributed volume rendering. RemoteVis then injects the transferred data into IndeX for rendering. IndeX is a scalable software capable of using multiple nodes and GPUs to render large volumes in full resolution. As such, we have coupled RemoteVis with slurm to dynamically schedule one or multiple HPC nodes to render any given dataset. RemoteVis was written in C/C++ and Python, providing an efficient API that requires only two functions to 1) start remote IndeX instances and 2) render regular volumes and point-cloud (diffraction) data on the web browser/Jupyter client.
slides icon Slides FRBL05 [12.680 MB]  
DOI • reference for this paper ※  
About • Received ※ 10 October 2021       Revised ※ 28 October 2021       Accepted ※ 20 November 2021       Issue date ※ 01 March 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)