Learning to problem-solve algorithmically, with concepts arising from parallelism and distribution, is now essential for student career success.  However, there are few examples of how to incorporate training for these skills into early undergraduate classes.

The Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER) is pleased to present the work of educators from a wide variety of teaching contexts who have developed and evaluated courses incorporating parallel and distributed computation as a natural aspect of teaching traditional college computing subjects. These detailed experiential reports serve as guidance for how others can follow in their steps and help shift the underlying paradigm of the computing curriculum into the 21st century. In addition to the material presented here, the authors also have contributed extensive appendices of teaching resources that are available online.

Topics and features:

●      Practical models for modernizing early and upper-level computing courses, from CS1/CS2 and data structures to systems, software engineering, GPU computing, cloud computing, and edge computing

●      Classroom-tested case studies showing how courses were designed, implemented, assessed, and refined in diverse institutional settings

●      Extensive online companion resources, including labs, assignments, projects, code, slides, and assessment materials

●      Flexible adoption pathways, supporting both incremental module-level infusion and full-course redesign

●       Coverage aligned with contemporary computing practice, including concurrency, asynchrony, accelerators, distributed systems, cloud services, and data-intensive applications

●       Application-driven treatment of core PDC ideas, connecting foundational concepts to AI, graphics, scientific computing, software systems, and edge/IoT domains

Sushil K. Prasad is a Professor of Computer Science at the University of Texas at San Antonio. He has carried out theoretical as well as experimental research in parallel and distributed computing, resulting in 150+ refereed publications, several patent applications, and about $8M in external research funds as principal investigator and over $14M overall (NSF/NIH/GRA/Industry). Sushil has been honored as an ACM Distinguished Scientist in Fall 2013 for his research on parallel data structures and applications. As IEEE TCPP Chair, he initiated and led an international effort to integrate parallel and distributed computing into undergraduate computing curricula.  Sushil was a Program Director at National Science Foundation during 2015-19. Anshul Gupta received a B.Tech. degree from the Indian Institute of Technology, New Delhi, in 1988 and a Ph.D. in 1995 from the University of Minnesota, both in Computer Science. He has coauthored several journal articles and conference papers on these topics and a textbook titled "Introduction to Parallel Computing." He is the primary author of Watson Sparse Matrix Package – a highly scalable and robust parallel solver for large sparse systems of linear equations.Alan Sussman is a Professor in the Department of Computer Science at the University of Maryland, and has been at Maryland since 1992. He is a founding member of the Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER). He is a subject area editor for the Parallel Computing journal and an associate editor for IEEE Transactions on Parallel and Distributed Systems. He was a program director in the Office of Advanced Cyberinfrastructure at the National Science Foundation from 2019 to 2022. Ramachandran Vaidyanathan is currently the Elaine T. and Donald C. Delaune Distinguished Professor in the Division of Electrical and Computer Engineering at the Louisiana State University, Baton Rouge. He has published extensively, including book and book chapters, patents, and journal and conference papers. He has served in leading roles for numerous conferences and workshops, and as associate editor in multiple journals, including the IEEE Transactions on Parallel and Distributed Systems, the Journal of Parallel and Distributed Computing, and the IEEE Transactions on Cloud Computing.Charles Weems is a Professor in the Manning College of Information and Computer Sciences there. He led development of two generations of a heterogeneous parallel processor for machine vision, called the Image Understanding Architecture, worked on caches for media and embedded processors, and multiprecision arithmetic on GPUs. Dr. Weems is a distinguished member of ACM, and a Senior Member of IEEE. He is a recipient of the IEEE Taylor Booth award for outstanding contributions to computer science education, the University Distinguished Teaching Award and the Distinguished Community Engagement Award for Teaching.