Title: Cloud and on-premises data center usage, expenditures, and approaches to return on investment: A survey of academic research computing organizations
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. more »« less
This report summarizes the discussions from a workshop convened at NSF on May 30-31, 2018 in Alexandria, VA. The overarching objective of the workshop was to rethink the nature and composition of the NSF-supported computational ecosystem given changing application requirements and resources and technology landscapes. The workshop included roughly 50 participants, drawn from high-performance computing (HPC) centers, campus computing facilities, cloud service providers (academic and commercial), and distributed resource providers. Participants spanned both large research institutions
and smaller universities.
Organized by Daniel Reed (University of Utah, chair), David Lifka (Cornell University), David Swanson (University of Nebraska), Rommie Amaro (UCSD), and Nancy Wilkins-Diehr (UCSD/SDSC), the workshop was motivated by the following observations. First, there have been dramatic changes in the number and nature of applications using NSF-funded resources, as well as their resource needs. As a result, there are new demands on the type (e.g., data centric) and location (e.g., close to the data or the users) of the resources as well as new usage modes (e.g., on-demand and elastic). Second, there have been dramatic changes in the landscape of technologies, resources, and delivery mechanisms, spanning large scientific instruments, ubiquitous sensors, and cloud services, among others.
Majumdar, Amit; Arora, Ritu(
, Second Workshop, SCEC 2018 Delhi, India, December 13–14, 2018 Proceedings)
Supercomputers are used to power discoveries and to reduce the time-to-results in a
wide variety of disciplines such as engineering, physical sciences, and healthcare. They
are globally considered as vital for staying competitive in defense, the financial sector,
several mainstream businesses, and even agriculture. An integral requirement for
enabling the usage of the supercomputers, like any other computer, is the availability
of the software. Scalable and efficient software is typically required for optimally using
the large-scale supercomputing platforms, and thereby, effectively leveraging the
investments in the advanced CyberInfrastructure (CI). However, developing and
maintaining such software is challenging due to several factors, such as, (1) no
well-defined processes or guidelines for writing software that can ensure
high-performance on supercomputers, and (2) shortfall of trained workforce having
skills in both software engineering and supercomputing. With the rapid advancement in
the computer architecture discipline, the complexity of the processors that are used in
the supercomputers is also increasing, and, in turn, the task of developing efficient
software for supercomputers is further becoming challenging and complex.
To mitigate the aforementioned challenges, there is a need for a common platform
that brings together different stakeholders from the areas of supercomputing and
software engineering. To provide such a platform, the second workshop on Software
Challenges to Exascale Computing (SCEC) was organized in Delhi, India, during
December 13–14, 2018.
The SCEC 2018 workshop informed participants about the challenges in large-scale
HPC software development and steered them in the direction of building international
collaborations for finding solutions to those challenges. The workshop provided a
forum through which hardware vendors and software developers can communicate
with each other and influence the architecture of the next-generation supercomputing
systems and the supporting software stack. By fostering cross-disciplinary associations,
the workshop served as a stepping-stone towards innovations in the future.
We are very grateful to the Organizing and Program Committees (listed below), the
sponsors (US National Science Foundation, Indian National Supercomputing Mission,
Atos, Mellanox, Centre for Development of Advanced Computing, San Diego
Supercomputing Center, Texas Advanced Computing Center), and the participants for
their contributions to making the SCEC 2018 workshop a success.
Daniel Lapine, Volodymyr Kindratenko(
, Practice and Experience in Advanced Research Computing (PEARC ’20))
In 2017, National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign (UIUC) established a pilot internship program for cyberinfrastructure (CI) professionals. The program, funded by NSF’s Office of Advanced Cyberinfrastructure (OAC) (award 1730519), was designed to address the shortage of a workforce with the specialized skills needed to support advanced CI operations. The program was envisioned to provide internship opportunities for individuals who want to gain first-hand experience in the CI operations at a supercomputing center, and develop and refine instructional materials to serve as a template that is openly distributed for use by other centers and institutions to train CI professionals. Program interns are selected from a pool of applicants with the main selection criteria of having a completed classwork equivalent to an associate degree and a
demonstrated interest in a career in CI operations. Interns work directly with a group of NCSA engineers in one of the areas of CI focus to gain hands-on experience in the deployment and operation of high-performance computing (HPC) infrastructure at a leading
HPC center. The expectation is that interns will enter a workforce that will develop, deploy, manage and support advanced CI at other universities, centers, and industry to meet the needs of the national computational science research community across academia and
industry.
Stewart, Craig A.; Costa, Claudia M.; Wernert, Julie A.; Snapp-Childs, Winona; Bland, Marques; Blood, Philip; Campbell, Terry; Couvares, Peter; Fischer, Jeremy; Hancock, David Y.; et al(
, Scientometrics)
Abstract
This paper uses accounting concepts—particularly the concept of Return on Investment (ROI)—to reveal the quantitative value of scientific research pertaining to a major US cyberinfrastructure project (XSEDE—the eXtreme Science and Engineering Discovery Environment). XSEDE provides operational and support services for advanced information technology systems, cloud systems, and supercomputers supporting non-classified US research, with an average budget for XSEDE of US$20M+ per year over the period studied (2014–2021). To assess the financial effectiveness of these services, we calculated a proxy for ROI, and converted quantitative measures of XSEDE service delivery into financial values using costs for service from the US marketplace. We calculated two estimates of ROI: a Conservative Estimate, functioning as a lower bound and using publicly available data for a lower valuation of XSEDE services; and a Best Available Estimate, functioning as a more accurate estimate, but using some unpublished valuation data. Using the largest dataset assembled for analysis of ROI for a cyberinfrastructure project, we found a Conservative Estimate of ROI of 1.87, and a Best Available Estimate of ROI of 3.24. Through accounting methods, we show that XSEDE services offer excellent value to the US government, that the services offered uniquely by XSEDE (that is, not otherwise available for purchase) were the most valuable to the facilitation of US research activities, and that accounting-based concepts hold great value for understanding the mechanisms of scientific research generally.
Effects of High Impact Educational Practices on Engineering and Computer Science Student Participation, Persistence, and Success at Land Grant Universities: Award# RIEF-1927218 – Year 2 Abstract Funded by the National Science Foundation (NSF), this project aims to investigate and identify associations (if any) that exist between student participation in High Impact Educational Practices (HIP) and their educational outcomes in undergraduate engineering and computer science (E/CS) programs. To understand the effects of HIP participation among E/CS students from groups historically underrepresented and underserved in E/CS, this study takes place within the rural, public university context at two western land grant institutions (one of which is an Hispanic-serving institution). Conceptualizing diversity broadly, this study considers gender, race and ethnicity, and first-generation, transfer, and nontraditional student status to be facets of identity that contribute to the diversity of academic programs and the technical workforce. This sequential, explanatory, mixed-methods study is guided by the following research questions: 1. To what extent do E/CS students participate in HIP? 2. What relationships (if any) exist between E/CS student participation in HIP and their educational outcomes (i.e., persistence in major, academic performance, and graduation)? 3. How do contextual factors (e.g., institutional, programmatic, personal, social, financial, etc.) affect E/CS student awareness of, interest in, and participation in HIP? During Project Year 1, a survey driven quantitative study was conducted. A survey informed by results of the National Survey of Student Engagement (NSSE) from each institution was developed and deployed. Survey respondents (N = 531) were students enrolled in undergraduate E/CS programs at either institution. Frequency distribution analyses were conducted to assess the respondents’ level of participation in extracurricular HIPs (i.e., global learning and study aboard, internships, learning communities, service and community-based learning, and undergraduate research) that have been shown in the literature to positively impact undergraduate student success. Further statistical analysis was conducted to understand the effects of HIP participation, coursework enjoyability, and confidence at completing a degree on the academic success of underrepresented and nontraditional E/CS students. Exploratory factor analysis was used to derive an "academic success" variable from five items that sought to measure how students persevere to attain academic goals. Results showed that a linear relationship in the target population exists and that the resultant multiple regression model is a good fit for the data. During the Project Year 2, survey results were used to develop focus group interview protocols and guide the purposive selection of focus group participants. Focus group interviews were conducted with a total of 27 undergraduates (12 males, 15 females, 16 engineering students, 11 computer science students) across both institutions via video conferencing (i.e., ZOOM) during the spring and fall 2021 semesters. Currently, verified focus group transcripts are being systematically analyzed and coded by a team of four trained coders to identify themes and answer the research questions. This paper will provide an overview of the preliminary themes so far identified. Future project activities during Project Year 3 will focus on refining themes identified during the focus group transcript analysis. Survey and focus group data will then be combined to develop deeper understandings of why and how E/CS students participate in the HIP at their university, taking into account the institutional and programmatic contexts at each institution. Ultimately, the project will develop and disseminate recommendations for improving diverse E/CS student awareness of, interest in, and participation in HIP, at similar land grant institutions nationally.
Chalker, Alan, Hillegas, Curtis W., Sill, Alan, Broude Geva, Sharon, and Stewart, Craig A. Cloud and on-premises data center usage, expenditures, and approaches to return on investment: A survey of academic research computing organizations. Retrieved from https://par.nsf.gov/biblio/10216559. PEARC '20: Practice and Experience in Advanced Research Computing . Web. doi:https://doi.org/10.1145/3311790.3396642.
Chalker, Alan, Hillegas, Curtis W., Sill, Alan, Broude Geva, Sharon, & Stewart, Craig A. Cloud and on-premises data center usage, expenditures, and approaches to return on investment: A survey of academic research computing organizations. PEARC '20: Practice and Experience in Advanced Research Computing, (). Retrieved from https://par.nsf.gov/biblio/10216559. https://doi.org/https://doi.org/10.1145/3311790.3396642
Chalker, Alan, Hillegas, Curtis W., Sill, Alan, Broude Geva, Sharon, and Stewart, Craig A.
"Cloud and on-premises data center usage, expenditures, and approaches to return on investment: A survey of academic research computing organizations". PEARC '20: Practice and Experience in Advanced Research Computing (). Country unknown/Code not available. https://doi.org/https://doi.org/10.1145/3311790.3396642.https://par.nsf.gov/biblio/10216559.
@article{osti_10216559,
place = {Country unknown/Code not available},
title = {Cloud and on-premises data center usage, expenditures, and approaches to return on investment: A survey of academic research computing organizations},
url = {https://par.nsf.gov/biblio/10216559},
DOI = {https://doi.org/10.1145/3311790.3396642},
abstractNote = {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.},
journal = {PEARC '20: Practice and Experience in Advanced Research Computing},
author = {Chalker, Alan and Hillegas, Curtis W. and Sill, Alan and Broude Geva, Sharon and Stewart, Craig A.},
}
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