Peachy parallel assignments (EduHPC 2023).



  • H. M. Bücker
  • J. Corrado
  • D. Fedorin
  • D. Garcia-Alvarez
  • A. Gonzalez-Escribano
  • J. Li
  • M. Pantoja
  • E. Pautsch
  • M. Plesske
  • M. Ponce
  • S. Rizzi
  • E. Saule
  • J. Schoder
  • G. Thiruvathukal
  • R. van Zon
  • W. Weber
  • D. P. Bunde


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.