Jupyter as Common Technology Platform for Interactive HPC Services

TL;DR

This paper discusses how the Minnesota Supercomputing Institute uses Jupyter as a unified platform for interactive HPC services, enhancing usability and workflow development while addressing resource management challenges.

Contribution

It introduces a novel approach of integrating Jupyter notebooks with traditional HPC systems to improve user experience and workflow flexibility.

Findings

Enhanced user accessibility to HPC resources

Successful integration of Jupyter with job scheduling systems

Insights into managing resource efficiency with interactive services

Abstract

The Minnesota Supercomputing Institute has implemented Jupyterhub and the Jupyter notebook server as a general-purpose point-of-entry to interactive high performance computing services. This mode of operation runs counter to traditional job-oriented HPC operations, but offers significant advantages for ease-of-use, data exploration, prototyping, and workflow development. From the user perspective, these features bring the computing cluster nearer to parity with emerging cloud computing options. On the other hand, retreating from fully-scheduled, job-based resource allocation poses challenges for resource availability and utilization efficiency, and can involve tools and technologies outside the typical core competencies of a supercomputing center's operations staff. MSI has attempted to mitigate these challenges by adopting Jupyter as a common technology platform for interactive services, capable of providing command-line, graphical, and workflow-oriented access to HPC resources while still integrating with job scheduling systems and using existing compute resources. This paper will describe the mechanisms that MSI has put in place, advantages for research and instructional uses, and lessons learned.