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Exploring Solutions for High-Performance Computing at ACM Institutions

Many of today’s most important research questions require analysis of large, complex datasets, too massive to be handled efficiently by a single computer.  High-performance computing (HPC) has become an essential tool for researchers and educators in a wide range of disciplines, including computer science, mathematics, statistics, natural sciences, social sciences, humanities, and the arts.

Research and instruction at many ACM institutions are constrained by having limited or no access to HPC resources. In an April 2019 survey completed by 111 respondents, 53 faculty at 10 ACM institutions indicated that they would use additional HPC resources in their research and/or teaching if these resources were available.

We present a proposal that supports two working sessions to be held at Lawrence University, where ACM faculty and technology personnel will meet to identify HPC needs, and develop an action plan toward addressing these needs and sharing the resources between ACM institutions.  By supporting this grant, the ACM would facilitate a dialogue between colleagues across disciplines and institutions that will aid the acquisition of new HPC resources, which will create new possibilities for future collaboration.

Goals

Limited computational resources is a significant challenge faced by liberal arts colleges. While most research universities have high performance computing (HPC) clusters, many liberal arts colleges, including most ACM institutions, lack access to HPC resources.  This limits the research accessible to faculty and makes it difficult for students to acquire computational skills that are transferable to a wide range of career fields.

In order to assess the need for additional HPC resources in ACM, we sent a survey through the ACM Deans of Faculty email listserv, and disseminated across campuses in early April 2019.  The survey garnered a total of 111 responses from ten institutions.  Nearly 50% of respondents identified a need for HPC resources in their research and more than 25% said they would integrate these resources in their teaching. All ten institutions indicated that they would use HPC resources in research and/or teaching, if these resources were available.

The goal of this proposed project is to expand access to HPC resources across the ACM. In order to accomplish this, we will:

  1. identify specific HPC needs at ACM campuses,
  2. explore and decide upon actionable solutions, and
  3. begin implementing the recommended strategy, which, for example, could include drafting an initial, consortium agreement for sharing resources and writing a large, multi-institutional external grant supporting the acquisition of HPC resources.

Final execution will likely be outside the scope of this grant, but the initial in-person collaboration will be essential to making partnership on HPC possible.

Activities

We propose to accomplish the project goals by hosting two working retreats at Lawrence.

The first workshop (June 2019), would include ACM faculty and technology personnel, along with an ACM representative and up to three paid consultants.  An invitation and application will be distributed throughout all ACM campuses and open to all faculty and technology staff.

We will select up to 28 participants from ACM institutions who express thoughtful interest, with the goal of securing diverse representation for all interested institutions.  Paid consultants would contribute concrete, HPC technical and curricular expertise to guide implementation recommendations.

The first working session will involve in-depth discussions on a variety of topics including:

  1. Computational needs and potential applications across campuses
  2. HPC solutions currently in place on some campuses
  3. Integration of HPC resources with existing curriculum and teaching resources
  4. Potential cross-institution research collaborations
  5. Pros and cons of implementing a HPC cluster and cloud-based alternatives
  6. Identifying the best possible external funding sources for HPC resources, if applicable

A second working session would then be held in Fall 2019. This workshop would involve a smaller set of up to ten participants, who will focus on implementing the task force’s recommendations.  This may include identifying the best source(s) of external funding, such as the National Science Foundation or Department of Defense, and writing a grant aimed at acquiring the HPC resources agreed upon at the first working session.

The expected, overall outcome of project activities is to encourage and facilitate multi-institutional discussion and strategic planning for acquiring and maintaining HPC resources that are affordable and usable for all ACM institutions.

An additional outcome for the entire project is enhanced relationships between faculty of different schools and disciplines whose work would benefit from additional HPC resources. Specific outcomes for each working session include:

First working session:

  • A shared understanding of existing ACM campus computing resources and needs
  • A shared understanding of our options for expanding consortium access to HPC
  • An analysis of and recommendation for how the ACM could expand HPC capacity most efficiently
  • If this includes establishing a consortium HPC cluster, an initial plan for location, maintenance, personnel, training, etc.
  • Recommendations for the best sources of external funding to acquire and maintain HPC resources
  • An action plan for the second working session and a list of potential participants

Second working session:

  • Drafting an initial, consortium agreement for sharing resources
  • An external grant proposal or other strategy for implementing the recommendations agreed upon in the first working session

In short, our proposed workshops will lead to increased knowledge, innovation, and partnership across the ACM.

Dissemination Strategies

In-person meetings will allow faculty across the ACM to share knowledge about computational solutions.  The computational resources currently available within the ACM varies across schools.  Learning about solutions currently in place at some institutions (i.e., internal servers, ways of acquiring access to external resources) will better position faculty at other institutions to pursue solutions appropriate for their college, or to identify existing solutions.

The multi-institutional nature of the HPC cluster will allow for collaboration between faculty and students across ACM colleges. Since representatives from all interested institutions will be involved in the initial stages of the project, they will be able to share the opportunities provided by an HPC solution with faculty at their respective institutions.  Once an HPC resource is established, a report on its existence and potential uses will be shared with deans of each college.

We will also create a website or blog containing information about the resources available and a user’s manual will be available online for all potential users. Periodic training sessions, held by faculty with expertise in using the cluster, would be held in order to assist new users.

 

Outcomes and Significance

Collaboration and innovation among faculty

This initiative would allow faculty and their institutions to take advantage of the economies of scale to explore an HPC solution for research and teaching that no one college could produce alone.  Working together as a task force and subsequently as users of shared HPC would facilitate collaboration.

For example, today, most STEM, peer-reviewed papers include multiple interdisciplinary authors, often from multiple institutions.  The ACM institutional framework is primed for expanding the collaborative nature of STEM research and education.

Access to HPC resources, such as a shared computational cluster, would facilitate interaction between students and faculty at multiple institutions when common scholarship interests are identified. Collaborators would be able to share files common to projects and access each other’s results more easily as a result of a shared computational cluster or other HPC resources.

The response to our faculty survey revealed that ACM faculty would use new HPC resources in creative ways. While many of the potential HPC users come from traditional mathematics and computer science backgrounds, we were excited to see that graphic designers, sociologists, geographers, and natural and physical scientists all expressed interest in using HPC resources, if they were available.

This highlights the strengths of the liberal arts model and clearly identifies the potential for interdisciplinary collaboration centered around the common use of HPC resources. New HPC resources would empower faculty and student researchers to consider broader, more innovative and impactful research questions, and enhance instruction potential, particularly in computational fields of study.

Reducing institutional costs, demonstrating value of high-quality liberal arts education

Currently, schools without access to a HPC cluster must rely on expensive external sources, such as cloud-based computing. Having access to a shared cluster, or other shared HPC resources, would provide ACM institutions with solutions at a fraction of the cost of paying for external computing.

Having one shared HPC instrument would decrease wasted down time that may happen if multiple institutions invest in their own setup. There are also possible personnel savings—instead of retraining current IT staff or hiring someone new at each institution with expertise to help maintain a HPC and provide faculty training, there could be as few as one person responsible to support faculty and students across the ACM.

Given the rapid increase in demand for graduates with expertise in data science, it is imperative that small liberal arts colleges (SLACs) equip students with the skills to acquire, store, and analyze massive and complex datasets.  Data science courses must provide students hands-on practice working with massive datasets, in order to prepare them for both the challenges of analyzing such datasets, and also the responsibilities of storing and maintaining large, often sensitive data.

Access to HPC resources will allow ACM schools to develop new computing classes and mentored research experiences in which undergraduates develop these skills, while working carefully with experienced faculty members.  Of the 111 respondents to our survey, 28 at six institutions indicated that they would find ways to integrate HPC resources into their course curriculum.

Advancing goals and priorities of participating campuses

At Lawrence, access to high-performance computing would further our institutional goals to advance excellence and innovation in research and teaching, as well as attract and retain faculty members and students.

Access to HPC resources has become an urgent priority as the research interests of several recent faculty appointees depends on HPC and as we work toward developing a new, cohesive, interdisciplinary curriculum around data science. As an institution, we want to participate in impactful, cutting-edge conversations and nurture collaboration; HPC is a tool that facilitates this objective.

At Carleton a small cluster exists for use by the college-wide community.  However, many researchers have been required to purchase their own servers to properly support their research agendas.  This model is not sustainable, does not properly support the wide variety of faculty who would benefit from computational resources, and is only accessible for the few faculty that can afford such resources.

Access to an integrated HPC system would allow a diverse set of faculty the support needed for high quality, collaborative research in a more robust, sustainable and economical fashion.

Grinnell likewise has a small compute cluster and a handful of faculty maintain their own relatively small HPC instruments.  Creating HPC access dovetails with a recent academic initiative to provide every student the opportunity for a significant research experience, the growth in statistics and data science curricula, and the explosion in computer science enrollments.

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