Preparing Students to Master Hybrid and Co-Processing Methods for High Performance Computing
- Conference
- Location
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Las Vegas, Nevada
- Publication Date
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April 18, 2024
- Start Date
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April 18, 2024
- End Date
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April 20, 2024
- Page Count
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16
- DOI
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10.18260/1-2--46055
- Permanent URL
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https://peer.asee.org/46055
- Download Count
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105
Paper Authors
Sam B Siewert
California State University, Chico
orcid.org/0000-0003-0258-5287
Dr. Sam Siewert has a B.S. in Aerospace and Mechanical Engineering from University of Notre Dame and M.S., Ph.D. in Computer Science from University of Colorado. He worked in the computer engineering industry for twenty-four years before starting an academic career in 2012. Dr. Siewert spent half of this time on NASA astronautics and deep space exploration programs and the next half on commercial product development for high performance networking and storage systems. In 2020, Dr. Siewert joined California State University Chico to teach computer science as full-time faculty and he continues in an adjunct professor role at University of Colorado Boulder. Research interests include real-time systems, interactive systems, machine vision and machine learning applied to sensor networks, sensor fusion, and instrumentation. Dr. Siewert is a co-founder of the Embedded Systems Engineering graduate program at
the University of Colorado and is a graduate curriculum committee chair at California State Chico.
Abstract
Students learning methods of parallel programming have a unique opportunity to develop a distinguished career in scalable scientific computing by learning scale-up, scale-out, and co-processing computer architecture. Teaching a course that covers traditional parallel programming methods for scalable high-performance computing has included scale-up shared memory methods such as OpenMP, scale-out distributed memory methods such as MPI (Message Passing Interface) and more recently co-processing methods such as CUDA (Compute Unified Device Architecture). Learning these three major methods is challenging for senior year undergraduate or first year graduate students, but now, quantum computing, and specifically quantum co-processing has emerged as another challenge for future high-performance computing software developers. It is accepted that quantum computing will show advantages in specific areas such as cryptanalysis, optimization, and quantum simulation, but will not soon replace traditional digital logic high performance computing anytime soon, if ever. Instead, much like a GP-GPU (General Purpose Graphics Processing Unit) acts as a co-processor for specific parallel work off-load, so will quantum computers, providing a QPU (Quantum Processing Unit). In this paper the methods for helping students deal with the triple challenge of learning parallel programming, writing correct code for any scale, and verifying scalability are presented along with new methods to incorporate quantum computing as another hybrid option. The paper provides details of how the triple challenge can be extended to include the fourth challenge of advanced co-processing methods. Techniques used are problem-based learning for mastery and contract-based projects where students demonstrate their achievement of key learning objectives.
Siewert, S. B. (2024, April), Preparing Students to Master Hybrid and Co-Processing Methods for High Performance Computing Paper presented at 2024 ASEE PSW Conference, Las Vegas, Nevada. 10.18260/1-2--46055
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