Year: 2025
Type: conference-paper
Abstract: This paper presents Snapgram, an innovative assignment for teaching matrix multiplications in the context of a Parallel Computing course. Designed for fourth-year undergraduate at the University of California, Davis, this assignment transforms the typically mundane task of matrix multiplication into an engaging exercise by framing it as a simple recommender system for a fictional social media application. Students implement two key operations: multiplying a matrix by itself and selecting the highest values in each row of the resulting matrix. These operations are adaptable to various parallel programming paradigms, including shared-memory (pthreads, OpenMP), distributed-memory (MPI), and GPU programming (CUDA). By contextualizing matrix multiplications within a practical, relatable scenario, Snapgram significantly enhances student engagement and learning outcomes. Instructors are typically encouraged to provide scaffolding code for I/O operations, allowing students to focus on implementing the core algorithms.
Author: Joël Porquet-Lupine
Institution: University of California, Davis
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4407686009