Likewise, center management should understand the benefits of enabling their staff to engage with open-source projects when possible, in this case Jupyter. HPC centers can engage with university researchers and application developers for help (e.g. the Pangeo project). Users should recognize that the shift to data science in supercomputing places new demands on HPC centers and that change takes time. They can probably best help by engaging at the agency level, explaining their needs for rich user interfaces to supercomputing to policymakers and program managers. This mechanism is what releases resources at the HPC center level to effect strategic change.
At NERSC our next step is to renew focus on supporting Jupyter at new scales and on new hardware. While we do a good job of meeting the needs of several hundred users per month, we need to do more to ease the barriers to entry to analytics cluster software and parallelized visualization tools. We look forward to this work on Perlmutter beginning this year.

Acknowledgments

This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231. We would like to thank the core Jupyter team, and especially our collaborators at U.C. Berkeley for technical guidance and support around the Jupyter and JupyterHub ecosystem.