Peachy Parallel Assignments (EduHPC 2023)

Written with H. Martin Bucker, Jeremiah Corrado, Daniel Fedorin, Diego Garcia-Alvarez, Arturo Gonzalez-Escribano, John Li, Maria Pantoja, Erik Pautsch, Marieke Plesske, Marcelo Ponce, Silvio Rizzi, Erik Saule, Johannes Schoder, George K. Thiruvathukal, Ramses van Zon, and Wolf Weber.
Proceedings of the 11th Workshop on Education for High-Performance Computing (EduHPC), 2023.


Abstract:

Peachy Parallel Assignments are model assignments for teaching parallel computing concepts. They are competitively selected for being adoptable by other instructors and "cool and inspirational" for students. Thus, they allow instructors to easily add high-quality assignments that will engage students to their classes.

This group of Peachy assignments features six new assignments. Students completing them will use k-Nearest Neighbor for classification, cluster using k-means, implement a data science pipeline of their choice, model traffic jams, apply parallel language features to solve the heat equation, and speed up a machine learning classification system.