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    Bioinformatics, Computational and Systems Biology

    (B-51) ParaView-ing Data at NERSC Remotely Using Jupyter Hub

    Thursday, October 12, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row B - Poster # 51

      Presenting Author(s)

    • KM

      Kayley McBride

      Student
      University of Central Oklahoma
      Edmond, Oklahoma, United States

      • Co-Author(s)

    • GX

      Gang Xu

      Professor of Biomedical Engineering
      University of Central Oklahoma, United States

      • Primary Investigator(s)

    • JB

      Johannes Blaschke

      Application Performance Specialist
      Lawrence Berkeley National Laboratory, United States

    Introduction:: ParaView is an open-source data analysis and visualization program that is used by researchers around the world to analyze complex biomedical datasets, containing things like a mixture of particles and fluids. Due to their size and complexity, these datasets cannot be easily analyzed with general-purpose tools like Microsoft Excel. Prior to this project, ParaView data at Lawrence Berkeley National Laboratory was exclusively analyzed using the ParaView graphic user interface (GUI) client. However, Jupyter is an increasingly popular web-based application that allows users to create and share documents containing code. This project is exploring ways to extend ParaView support in Jupyter.

    Materials and Methods:: ParaView was made from the source in a Docker container. The container was then put on Perlmutter to allow the ParaView server (pvserver) to be accessed by the IParaView Kernel. The IParaView Kernel designed by Kitware was utilized. Edits were made to the kernel to allow implementation on the National Energy Research Scientific Computing Center (NERSC) network.

    Results, Conclusions, and Discussions:: The IParaView Kernel was successfully implemented on Jupyter Hub. This was accomplished by using a combination of the pvserver cluster to manage the pvservers as well as the Super Facility API to allow the servers to run on the compute nodes on Perlmutter. This fully implemented kernel will allow the researchers at Lawrence Berkeley National Lab to focus more on doing experiments rather than spending large amounts of time attempting to analyze the data from previous experiments. In turn the biomedical research that is occurring will be able to happen at a faster rate allowing these important projects to move forward quicker.

    Acknowledgements (Optional): :

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