Deploying Jupyter Notebooks at scale on XSEDE resources for Science Gateways and workshops

Jupyter Notebooks have become a mainstream tool for interactive computing in every field of science. Jupyter Notebooks are suitable as companion applications for Science Gateways, providing more flexibility and post-processing capability to the users. Moreover they are often used in training events and workshops to provide immediate access to a pre-configured interactive computing environment. The Jupyter team released the JupyterHub web application to provide a platform where multiple users can login and access a Jupyter Notebook environment. When the number of users and memory requirements are low, it is easy to setup JupyterHub on a single server. However, setup becomes more complicated when we need to serve Jupyter Notebooks at scale to tens or hundreds of users. In this paper we will present three strategies for deploying JupyterHub at scale on XSEDE resources. All options share the deployment of JupyterHub on a Virtual Machine on XSEDE Jetstream. In the first scenario, JupyterHub connects to a supercomputer and launches a single node job on behalf of each user and proxies back the Notebook from the computing node back to the user's browser. In the second scenario, implemented in the context of a XSEDE consultation for the IRIS consortium for Seismology, we deploy Docker in Swarm mode to coordinate many XSEDE Jetstream virtual machines to provide Notebooks with persistent storage and quota. In the last scenario we install the Kubernetes containers orchestration framework on Jetstream to provide a fault-tolerant JupyterHub deployment with a distributed filesystem and capability to scale to thousands of users. In the conclusion section we provide a link to step-by-step tutorials complete with all the necessary commands and configuration files to replicate these deployments.Comment: 7 pages, 3 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers

For decades, the use of HPC systems was limited to those in the physical sciences who had mastered their domain in conjunction with a deep understanding of HPC architectures and algorithms. During these same decades, consumer computing device advances produced tablets and smartphones that allow millions of children to interactively develop and share code projects across the globe. As the HPC community faces the challenges associated with guiding researchers from disciplines using high productivity interactive tools to effective use of HPC systems, it seems appropriate to revisit the assumptions surrounding the necessary skills required for access to large computational systems. For over a decade, MIT Lincoln Laboratory has been supporting interactive, on-demand high performance computing by seamlessly integrating familiar high productivity tools to provide users with an increased number of design turns, rapid prototyping capability, and faster time to insight. In this paper, we discuss the lessons learned while supporting interactive, on-demand high performance computing from the perspectives of the users and the team supporting the users and the system. Building on these lessons, we present an overview of current needs and the technical solutions we are building to lower the barrier to entry for new users from the humanities, social, and biological sciences.Comment: 15 pages, 3 figures, First Workshop on Interactive High Performance Computing (WIHPC) 2018 held in conjunction with ISC High Performance 2018 in Frankfurt, German

Workshop Report: Container Based Analysis Environments for Research Data Access and Computing

Report of the first workshop on Container Based Analysis Environments for Research Data Access and Computing supported by the National Data Service and Data Exploration Lab and held at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign

Rosetta: a container-centric science platform for resource-intensive, interactive data analysis

Rosetta is a science platform for resource-intensive, interactive data analysis which runs user tasks as software containers. It is built on top of a novel architecture based on framing user tasks as microservices - independent and self-contained units - which allows to fully support custom and user-defined software packages, libraries and environments. These include complete remote desktop and GUI applications, besides common analysis environments as the Jupyter Notebooks. Rosetta relies on Open Container Initiative containers, which allow for safe, effective and reproducible code execution; can use a number of container engines and runtimes; and seamlessly supports several workload management systems, thus enabling containerized workloads on a wide range of computing resources. Although developed in the astronomy and astrophysics space, Rosetta can virtually support any science and technology domain where resource-intensive, interactive data analysis is required

Workflow models for heterogeneous distributed systems

The role of data in modern scientific workflows becomes more and more crucial. The unprecedented amount of data available in the digital era, combined with the recent advancements in Machine Learning and High-Performance Computing (HPC), let computers surpass human performances in a wide range of fields, such as Computer Vision, Natural Language Processing and Bioinformatics. However, a solid data management strategy becomes crucial for key aspects like performance optimisation, privacy preservation and security. Most modern programming paradigms for Big Data analysis adhere to the principle of data locality: moving computation closer to the data to remove transfer-related overheads and risks. Still, there are scenarios in which it is worth, or even unavoidable, to transfer data between different steps of a complex workflow. The contribution of this dissertation is twofold. First, it defines a novel methodology for distributed modular applications, allowing topology-aware scheduling and data management while separating business logic, data dependencies, parallel patterns and execution environments. In addition, it introduces computational notebooks as a high-level and user-friendly interface to this new kind of workflow, aiming to flatten the learning curve and improve the adoption of such methodology. Each of these contributions is accompanied by a full-fledged, Open Source implementation, which has been used for evaluation purposes and allows the interested reader to experience the related methodology first-hand. The validity of the proposed approaches has been demonstrated on a total of five real scientific applications in the domains of Deep Learning, Bioinformatics and Molecular Dynamics Simulation, executing them on large-scale mixed cloud-High-Performance Computing (HPC) infrastructures

Using Containers to Create More Interactive Online Training and Education Materials

Containers are excellent hands-on learning environments for computing topics because they are customizable, portable, and reproducible. The Cornell University Center for Advanced Computing has developed the Cornell Virtual Workshop in high performance computing topics for many years, and we have always sought to make the materials as rich and interactive as possible. Toward the goal of building a more hands-on experimental learning experience directly into web-based online training environments, we developed the Cornell Container Runner Service, which allows online content developers to build container-based interactive edit and run commands directly into their web pages. Using containers along with CCRS has the potential to increase learner engagement and outcomes.Comment: 10 pages, 3 figures, PEARC '20 conference pape

Evaluating and Enabling Scalable High Performance Computing Workloads on Commercial Clouds

Performance, usability, and accessibility are critical components of high performance computing (HPC). Usability and performance are especially important to academic researchers as they generally have little time to learn a new technology and demand a certain type of performance in order to ensure the quality and quantity of their research results. We have observed that while not all workloads run well in the cloud, some workloads perform well. We have also observed that although commercial cloud adoption by industry has been growing at a rapid pace, its use by academic researchers has not grown as quickly. We aim to help close this gap and enable researchers to utilize the commercial cloud more efficiently and effectively. We present our results on architecting and benchmarking an HPC environment on Amazon Web Services (AWS) where we observe that there are particular types of applications that are and are not suited for the commercial cloud. Then, we present our results on architecting and building a provisioning and workflow management tool (PAW), where we developed an application that enables a user to launch an HPC environment in the cloud, execute a customizable workflow, and after the workflow has completed delete the HPC environment automatically. We then present our results on the scalability of PAW and the commercial cloud for compute intensive workloads by deploying a 1.1 million vCPU cluster. We then discuss our research into the feasibility of utilizing commercial cloud infrastructure to help tackle the large spikes and data-intensive characteristics of Transportation Cyberphysical Systems (TCPS) workloads. Then, we present our research in utilizing the commercial cloud for urgent HPC applications by deploying a 1.5 million vCPU cluster to process 211TB of traffic video data to be utilized by first responders during an evacuation situation. Lastly, we present the contributions and conclusions drawn from this work