COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain experts, mathematical modelers, and scientific computing specialists. Computationally, however, it also revealed critical gaps in the ability of researchers to exploit advanced computing systems. These challenging areas include gaining access to scalable computing systems, porting models and workflows to new systems, sharing data of varying sizes, and producing results that can be reproduced and validated by others. Informed by our team’s work in supporting public health decision makers during the COVID-19 pandemic and by the identified capability gaps in applying high-performance computing (HPC) to the modeling of complex social systems, we present the goals, requirements, and initial implementation of OSPREY, an open science platform for robust epidemic analysis. The prototype implementation demonstrates an integrated, algorithm-driven HPC workflow architecture, coordinating tasks across federated HPC resources, with robust, secure and automated access to each of the resources. We demonstrate scalable and fault-tolerant task execution, an asynchronous API to support fast time-to-solution algorithms, an inclusive, multi-language approach, and efficient wide-area data management. The example OSPREY code is made available on a public repository.
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Evaluating the Effectiveness of an Online Learning Platform in Transitioning Users from a High Performance Computing to a Commercial Cloud Computing Environment
Developments in large scale computing environments have led to design of workflows that rely on containers and analytics platform that are well supported by the commercial cloud. The National Science Foundation also envisions a future in science and engineering that includes commercial cloud service providers (CSPs) such as Amazon Web Services, Azure and Google Cloud. These twin forces have made researchers consider the commercial cloud as an alternative option to current high performance computing (HPC) environments. Training and knowledge on how to migrate workflows, cost control, data management, and system administration remain some of the commonly listed concerns with adoption of cloud computing. In an effort to ameliorate this situation, CSPs have developed online and in-person training platforms to help address this problem. Scalability, ability to impart knowledge, evaluating knowledge gain, and accreditation are the core concepts that have driven this approach. Here, we present a review of our experience using Google’s Qwiklabs online platform for remote and in-person training from the perspective of a HPC user. For this study, we completed over 50 online courses, earned five badges and attended a one-day session. We identify the strengths of the approach, identify avenues to refine them, and consider means to further community engagement. We further evaluate the readiness of these resources for a cloud-curious researcher who is familiar with HPC. Finally, we present recommendations on how the large scale computing community can leverage these opportunities to work with CSPs to assist researchers nationally and at their home institutions.
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- NSF-PAR ID:
- 10179043
- Date Published:
- Journal Name:
- Journal of computational science education
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2153-4136
- Page Range / eLocation ID:
- 93-99
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.22369/issn.2153-4136/11/1/15
