Hands-on PDC in Undergraduate Computing Education
- URL: http://arxiv.org/abs/2604.25812v1
- Date: Tue, 28 Apr 2026 16:20:37 GMT
- Title: Hands-on PDC in Undergraduate Computing Education
- Abstract summary: This paper presents a practical assignment in which students engage directly with the University of Florida's HiPerGator supercomputer.<n>Students navigate batch scheduling, core allocation, and performance tuning, experiences that are rarely accessible at the undergraduate level.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Parallel and Distributed Computing (PDC) is a critical yet conceptually challenging area of the undergraduate computer science curriculum. While students often encounter these concepts in theory, few gain exposure to experience in real high-performance computing (HPC) environments. Research shows that when students are engaged in project-based learning they retain knowledge more effectively. They also develop a deeper understanding of concepts taught in the classroom. This paper presents a practical assignment in which students engage directly with the University of Florida's HiPerGator supercomputer to implement and benchmark matrix multiplication using Python and C (via POSIX threads and OpenMP). Students navigate batch scheduling, core allocation, and performance tuning, experiences that are rarely accessible at the undergraduate level. We describe the assignment in detail and provide a three-year evaluation across multiple course offerings, highlighting how structured access to real HPC infrastructure can deepen student understanding of parallelism and multithreading.
Related papers
- Teaching Quantum Computing through Lab-Integrated Learning: Bridging Conceptual and Computational Understanding [0.6660458629649826]
Quantum computing education requires students to develop reasoning skills grounded in probability, measurement, and interference.<n>This paper reports on the design and delivery of a combined undergraduate and graduate course at Louisiana State University that employed a lab-integrated learning model.
arXiv Detail & Related papers (2025-10-17T00:29:52Z) - Project-Based Learning in Introductory Quantum Computing Courses: A Case Study on Quantum Algorithms for Medical Imaging [0.0]
This paper demonstrates how Project-Based Learning can be leveraged to bridge that gap.<n>This can be done by engaging students in a real-world, interdisciplinary task that combines quantum computing with their field of interest.
arXiv Detail & Related papers (2025-08-29T04:24:26Z) - FlashHack: Reflections on the Usage of a Micro Hackathon as an Assessment Tool in a Machine Learning Course [0.0]
Group project-based learning is an increasingly popular form of experiential learning in CS education.
To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon.
Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors.
arXiv Detail & Related papers (2024-10-07T11:21:11Z) - Benchmarking Predictive Coding Networks -- Made Simple [48.652114040426625]
We tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning.<n>We propose a library, called PCX, that focuses on performance and simplicity, and use it to implement a large set of standard benchmarks.<n>We perform extensive tests on such benchmarks using both existing algorithms for PCNs, as well as adaptations of other methods popular in the bio-plausible deep learning community.
arXiv Detail & Related papers (2024-07-01T10:33:44Z) - UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course.
We introduce UKP-SQuARE as a platform for QA education.
Students can run, compare, and analyze various QA models from different perspectives.
arXiv Detail & Related papers (2023-05-31T11:29:04Z) - Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels.
We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion.
We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
arXiv Detail & Related papers (2023-04-25T05:04:44Z) - Embodied Concept Learner: Self-supervised Learning of Concepts and
Mapping through Instruction Following [101.55727845195969]
We propose Embodied Learner Concept (ECL) in an interactive 3D environment.
A robot agent can ground visual concepts, build semantic maps and plan actions to complete tasks.
ECL is fully transparent and step-by-step interpretable in long-term planning.
arXiv Detail & Related papers (2023-04-07T17:59:34Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - Understanding Learners' Problem-Solving Strategies in Concurrent and
Parallel Programming: A Game-Based Approach [5.2998037386426615]
This paper presents our results of using Parallel, an educational game about concurrent and parallel programming.
Based on a study of 44 undergraduate students, our research shows that self-efficacy increased significantly after playing the game.
arXiv Detail & Related papers (2020-05-10T21:29:10Z) - Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits [87.84166068337076]
We propose Einsum Networks (EiNets), a novel implementation design for PCs.<n>At their core, EiNets combine a large number of arithmetic operations in a single monolithic einsum-operation.<n>We show that the implementation of Expectation-Maximization (EM) can be simplified for PCs, by leveraging automatic differentiation.
arXiv Detail & Related papers (2020-04-13T23:09:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.