EduHPC-23 Technical Program
Monday, November 13, 2023
Room 506, Colorado Convention Center, Denver, CO
Opening Invited Talk
Educating Post Exascale HPC Leaders
Kathy Yelick
Robert S. Pepper Distinguished Professor of Electrical Engineering and Computer Sciences and Vice Chancellor for Research at University of California Berkeley
The first generation of exascale computing systems is coming online along with new application capabilities and system software. At the same time, demands for high performance computing continue to grow for more powerful simulations, adoption of machine learning methods, and huge data analysis problems arising from new instruments and increasingly ubiquitous devices. In its broadest sense, computational science research is expanding beyond physical and life sciences into social sciences, public policy, and even the humanities.
Concurrent with these trends, chip technology is facing scaling limits, making it increasingly difficult to meet these new demands. Disruptions in the computing marketplace, which include supply chain limitations, a shrinking set of system integrators, and the growing influence of cloud providers are changing underlying assumptions about how to acquire and deploy future supercomputers. At the same time, there are discussions around the role of AI/ML and quantum computing.
How do we educate students for a post-Exascale world? A finite set of computational motifs represent much of the parallel computing workload in modeling and simulation. Should the HPC community focus on those or should they be expanded to include data analytics and machine learning approaches? Finally, what are the workforce needs for the future of high end computing?
Paper Session 1: Pedagogical Techniques for PDC
Alan Sussman, Univ. of Maryland
8 minutes per talk; 12 minutes panel Q&A
Teaching Heterogeneous and Parallel Computing with Google Colab and Raspberry Pi Clusters (PPT Link)
Zhiguang Xu
Infrastructure for Writing Fork-Join Tests (PPT Link)
Prasun Dewan
Data-Driven Discovery of Anchor Points for PDC Content, (PPT Link)
Matthew McQuaigue, Erik Saule, Kalpathi Subramanian and Jamie Payton
AutoLearn: Learning in the Edge to Cloud Continuum, (PPT Link)
Alicia Esquivel Morel, William Fowler, Kate Keahey, Kyle Zheng, Michael Sherman and Richard Anderson
Lightning Talks
George K. Thiruvathukal, Loyola University Chicago
3 minutes per talk; 8 minutes panel Q&A
Melton, Melesse, Vergara, Parete-Koon
Training Experiences by Skills for HPC Ecosystems,
Barrios Hernandez, Diaz
Teaching Non-determinism in High Performance Applications,
Marquez, Bogale, Pandey, Tan, Whitnah, Bhowmick, Taufer
Machine Learning Movie Night: A Pilot Machine Learning Course for High-School Students,
Jaffe
The World’s Worst Optical NIC,
Ellsworth
Alexander
Adding Sustainability to Parallel Programming Assignments,
Elster
Paper Session 2: Experience Reports
Charles Weems, UMass Amherst
8 minutes per talk; 12 minutes panel Q&A
The Wide Area Classroom: 24,000 HPC Students and Growing, (PPT Link)
Valerie Rossi, John Urbanic and Thomas Maiden
Faculty Development Workshops for Integrating PDC in Early Undergraduate Curricula: An Experience Report,David Brown, Sheikh Ghafoor, Mike Rogers and Ada Haynes (PPT Link)
An NSF REU Site Based on Trust and Reproducibility of Intelligent Computation: Experience Report (PPT Link)
Mary Hall, Ganesh Gopalakrishnan, Eric Eide, Johanna Cohoon, Jeff M. Phillips, Mu Zhang, Shireen Y. Elhabianm, Aditya Bhaskara, Harvey Dam, Artem Yadrov, Tushar Kataria, Amir Mohammad Tavakkoli, Sameeran Joshi and Mokshagna Sai Teja Karanam
Performance Engineering for Graduate Students: a View from Amsterdam, (PPT Link)
Ana Lucia Varbanescu and Stephen Nicholas Swatman
Peachy Parallel Assignments
David Bunde, Knox College
4 minutes per talk; 5 minutes panel Q&A
Using MPI For Distributed Hyper-Parameter Optimization And Uncertainty Evaluation,
Pantoja, Pautsch, Li, Rizzi, Thiruvathukal
1D Heat Equation in Chapel,
Corrado
k-Nearest Neighbor with Map Reduce MPI,
Saule
Parallelizing a 1-Dim Nagel-Schreckenberg Traffic,
van Zon, Ponce
Program Your Favorite Data Science Pipeline in Spark,
Bücker, Plesske, Schoder, Weber
K-means clustering: An assignment for OpenMP, MPI, and CUDA/OpenCL,
García-Álvarez, Gonzalez-Escribano
Mark Your Calendar: CDER Announcements
Closing
Apan Qasem , TXST