The Blue Waters Student Internship Program: Promoting Competence and Confidence for Next Generation Researchers in High-Performance Computing

Jacobs, Patricia; List, Phillip; Ludin, Mobeen; Weeden, Aaron; Panoff, Robert M.

doi:10.1109/eduhpc.2014.6

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…The Blue Waters Student Internship Program was launched in 2009 and continued through 2019 to provide undergraduate students across the U.S. with training, a year of financial support, access to leading-edge petascale HPC resources, and the opportunity to work with a mentor on a computational research project. The program was managed by the Shodor Education Foundation, Inc. We previously reported details of the program in 2014 [8].…”

Section: Student Internshipsmentioning

confidence: 99%

Impact of Blue Waters Education and Training

Kramer¹,

Lathrop²,

Gordon³

et al. 2022

JOCSE

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The Blue Waters proposal to NSF, entitled "Leadership-Class Scientific and Engineering Computing: Breaking Through the Limits," identified education and training as essential components for the computational and data analysis research and education communities. The Blue Waters project began in 2007, the petascale computing system began operations on March 28, 2013, and the system served the community longer than originally planned as it was decommissioned in January 2022. This paper contributes to the Blue Waters project's commitment to document the lessons learned and longitudinal impact of its activities.The Blue Waters project pursued a broad range of workforce development activities to recruit, engage, and support a diverse mix of students, educators, researchers, and developers across the U.S. The focus was on preparing the current and future workforce to contribute to advancing scholarship and discovery using computational and data analytics resources and services. Formative and summative evaluations were conducted to improve the activities and track the impact.Many of the lessons learned have been implemented by the National Center for Supercomputing Applications (NCSA) and the New Frontiers Initiative (NFI) at the University of Illinois, and by other organizations. We are committed to sharing our experiences with other organizations that are working to reproduce, scale up, and/or sustain activities to prepare the computational and data analysis workforce.

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Section: Student Internshipsmentioning

confidence: 99%

Impact of Blue Waters Education and Training

Kramer¹,

Lathrop²,

Gordon³

et al. 2022

JOCSE

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show abstract

“…Shodor maintains an in-house application website [9] and database, originally developed to coordinate workshop registrations for the National Computational Science Institute [7]. Student and mentor application forms for EMPOWER have been developed based on those of the Blue Waters Student Internship Program [4]. The mentor form requests the mentor's affiliation/role with XSEDE, the project title and summary, student job description, use of XSEDE resources, contribution to the community, start and end dates, location, participation level, training plan, number of students the mentor can support, student names (if already identified), and additional student prerequisites and qualifications.…”

Section: Receiving and Reviewing Applicationsmentioning

confidence: 99%

XSEDE EMPOWER: Engaging Undergraduates in the Work of Advanced Digital Services and Resources

Weeden¹

2021

JOCSE

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To address the need for a diverse and capable workforce in advanced digital services and resources, the Shodor Education Foundation has been coordinating an undergraduate student program for the Extreme Science and Engineering Discovery Environment (XSEDE). The name of the program is EMPOWER (Expert Mentoring Producing Opportunities for Work, Education, and Research). The goal of the program is to engage a diverse group of undergraduate students in the work of XSEDE, matching them with faculty and staff mentors who have projects that make use of XSEDE services and resources or that otherwise prepare students to use these types of services and resources. Mentors have coordinated projects in computational science and engineering research in many fields of study as well as systems and user support. Students work for a semester, quarter, or summer at a time and can participate for up to a year supported by stipends from the program, at different levels depending on experience. The program has run for 11 iterations from summer 2017 through fall 2020. The 111 total student participants have been 28% female and 31% underrepresented minority, and they have been selected from a pool of 272 total student applicants who have been 31% female and 30% underrepresented minority. We are pleased that the selection process does not favor against women and minorities but would also like to see these proportions increase. At least one fourth of the students have presented their work in articles or at conferences, and multiple credit the program with moving them towards graduate study or otherwise advancing them in their careers.

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“…Considering a cutoff radius can simplify the calculation, as the force between each object and any other object outside this radius is automatically set to zero. For the purpose of this study, the GalaxSee n-body model [19] is used as the simulation module.…”

Section: Test Problem 2: the N-body Galaxy Modelmentioning

confidence: 99%

Kernel optimization for Low-Rank Multi-Fidelity Algorithms

Razi,

Kirby,

Narayan

2021

Preprint

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One of the major challenges for low-rank multi-fidelity (MF) approaches is the assumption that low-fidelity (LF) and high-fidelity (HF) models admit "similar" low-rank kernel representations. Low-rank MF methods have traditionally attempted to exploit low-rank representations of linear kernels, which are kernel functions of the form K(u, v) = v T u for vectors u and v. However, such linear kernels may not be able to capture low-rank behavior, and they may admit LF and HF kernels that are not similar. Such a situation renders a naive approach to low-rank MF procedures ineffective. In this paper, we propose a novel approach for the selection of a near-optimal kernel function for use in low-rank MF methods. The proposed framework is a two-step strategy wherein: (1) hyperparameters of a library of kernel functions are optimized, and (2) a particular combination of the optimized kernels is selected, through either a convex mixture (Additive Kernel Approach) or through a data-driven optimization (Adaptive Kernel Approach). The two resulting methods for this generalized framework both utilize only the available inexpensive low-fidelity data and thus no evaluation of high-fidelity simulation model is needed until a kernel is chosen. These proposed approaches are tested on five non-trivial real-world problems including multi-fidelity surrogate modeling for one-and two-species molecular systems, gravitational many-body problem, associating polymer networks, plasmonic nano-particle arrays, and an incompressible flow in channels with stenosis. The results for these numerical experiments demonstrate the numerical stability efficiency of both proposed kernel function selection procedures, as well as high accuracy of their resultant predictive models for estimation of quantities of interest. Comparisons against standard linear kernel procedures also demonstrate increased accuracy of the optimized kernel approaches.

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The Blue Waters Student Internship Program: Promoting Competence and Confidence for Next Generation Researchers in High-Performance Computing

Cited by 7 publications

References 3 publications

Impact of Blue Waters Education and Training

Impact of Blue Waters Education and Training

XSEDE EMPOWER: Engaging Undergraduates in the Work of Advanced Digital Services and Resources

Kernel optimization for Low-Rank Multi-Fidelity Algorithms

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