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Open OnDemand (openondemand.org) is an NSF-funded open-source HPC platform currently in use at over 200 HPC centers around the world. It is an intuitive, innovative, and interactive interface to remote computing resources. Open OnDemand (OOD) helps computational researchers and students efficiently utilize remote computing resources by making them easy to access from any device. It helps computer center staff support a wide range of clients by simplifying the user interface and experience. 

The need for distance and virtual learning platforms has been emphasized by the COVID-19 pandemic. With the closure of campuses in Spring of 2020 and many classes moving to online only in Fall of 2020, platforms for facilitating computationally oriented curriculum have had to be quickly adopted. Open OnDemand offers a familiar web-based portal to computational resources such as high-performance computing and cloud. Through OnDemands customizable dashboard, students can be offered an interface tailored to the course schedule giving them a just what I need view. Advantages to instructors include a web accessible, platform agnostic interface leading to less time for troubleshooting local student platforms and more time for discussion of the core course curriculum, a fully customizable course page, access controls, and more. Here we present Open OnDemand as a platform for developing, deploying, and presenting software and course material to software-oriented classes as used at Ohio Supercomputer Center and Virginia Tech. 

The landscape of research in science and engineering is heavily reliant on computation and data processing. There is continued and expanded usage by disciplines that have historically used advanced computing resources, new usage by disciplines that have not traditionally used HPC, and new modalities of the usage in Data Science, Machine Learning, and other areas of AI. Along with these new patterns have come new advanced computing resource methods and approaches, including the availability of commercial cloud resources. The Coalition for Academic Scientific Computation (CASC) has long been an advocate representing the needs of academic researchers using computational resources, sharing best practices and offering advice to create a national cyberinfrastructure to meet US science, engineering, and other academic computing needs. CASC has completed the first of what we intend to be an annual survey of academic cloud and data center usage and practices in analyzing return on investment in cyberinfrastructure. Critically important findings from this first survey include the following: many of the respondents are engaged in some form of analysis of return in research computing investments, but only a minority currently report the results of such analyses to their upper-level administration. Most respondents are experimenting with use of commercial cloud resources but no respondent indicated that they have found use of commercial cloud services to create financial benefits compared to their current methods. There is clear correlation between levels of investment in research cyberinfrastructure and the scale of both cpu core-hours delivered and the financial level of supported research grants. Also interesting is that almost every respondent indicated that they participate in some sort of national cooperative or nationally provided research computing infrastructure project and most were involved in academic computing-related organizations, indicating a high degree of engagement by institutions of higher education in building and maintaining national research computing ecosystems. Institutions continue to evaluate cloud-based HPC service models, despite having generally concluded that so far cloud HPC is too expensive to use compared to their current methods. 

High performance computing workloads often benefit from human in the loop interactions. Steps in complex pipelines ranging from quality control to parameter adjustments are critical to the successful and efficient completion of modern problems. We give several example workflows in bioinformatics and deep learning where computing decisions are made throughout the processing pipelines ultimately changing the course of the compute. We also show how users can interact with the pipeline using Open OnDemand plus XDMoD or Plot.ly. 

High performance computing (HPC) has led to remarkable advances in science and engineering and has become an indispensable tool for research. Unfortunately, HPC use and adoption by many researchers is often hindered by the complex way these resources are accessed. Indeed, while the web has become the dominant access mechanism for remote computing services in virtually every computing area, HPC is a notable exception. Open OnDemand is an open source project negating this trend by providing web‐based access to HPC resources (https://openondemand.org). This article describes the challenges to adoption and other lessons learned over the 3‐year project that may be relevant to other science gateway projects. We end with a description of future plans the project team has during the Open OnDemand 2.0 project including specific developments in machine learning and GPU monitoring.