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1. Message from the EduHPC-24 Workshop Chairs

   Prasad, Sushil K; Thiruvathukal, George K; Saule, Erik; Puri, Satish; Bunde, David (February 2025, IEEE Press)

   Welcome to the proceedings of EduHPC24, the Workshop on Education for High Performance Computing, held in Atlanta, Georgia on November 17, 2024, in conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis (SC24). EduHPC has been a regular workshop of the SC conference since 2013, devoted to the development of reproducible educational and curricular innovations and resources for undergraduate and graduate education in High Performance Computing (HPC) and Parallel and Distributed Computing (PDC). The workshop particularly focuses on connecting individuals from academia, industry, national laboratories, and funding agencies with the goal of exchanging ideas on the enhancement and infusion of HPC, PDC, and Big Data education. EduHPC is in coordination with the IEEE TCPP curriculum initiative on parallel and distributed computing (http://www.cs.gsu.edu/~tcpp/curriculum) for computer science and computer engineering undergraduates and is supported by NSF and the NSF-supported Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER). 

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2. NSF/IEEE-TCPP Curriculum on Parallel and Distributed Computing for Undergraduates - Version II - Big Data, Energy, and Distributed Computing

   https://doi.org/10.1145/3545947.3569594  

   Prasad, Sushil; Weems, Charles; Sussman, Alan; Gupta, Anshul; Estrada, Trilce; Vaidyanathan, Ramachandran; Ghafoor, Sheikh; Kant, Krishna; Stunkel, Craig (March 2022, ACM)

   This special session will report on the updated NSF/IEEE-TCPP Curriculum on Parallel and Distributed Computing released in Nov 2020 by the Center for Parallel and Distributed Computing Curricu- lum Development and Educational Resources (CDER). The purpose of the special session is to obtain SIGCSE community feedback on this curriculum in a highly interactive manner employing the hybrid modality and supported by a full-time CDER booth for the duration of SIGCSE. In this era of big data, cloud, and multi- and many-core systems, it is essential that the computer science (CS) and computer engineering (CE) graduates have basic skills in par- allel and distributed computing (PDC). The topics are primarily organized into the areas of architecture, programming, and algo- rithms topics. A set of pervasive concepts that percolate across area boundaries are also identified. Version 1 of this curriculum was released in December 2012. That curriculum guideline has over 140 early adopter institutions worldwide and has been incorpo- rated into the 2013 ACM/IEEE Computer Science curricula. This Version-II represents a major revision. The updates have focused on enhancing coverage related to the topical aspects of Big Data, Energy, and Distributed Computing. The session will also report on related CDER activities including a workshop series on a PDC institute conceptualization, developing a CE-oriented version of the curriculum, and identifying a minimal set of PDC topics aligned with ABET’s exposure-level PDC require- ments. The interested SIGCSE audience includes educators, authors,publishers, curriculum committee members, department chairs and administrators, professional societies, and the computing industry. 

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3. Classifying Pedagogical Material to Improve Adoption of Parallel and Distributed Computing Topics

   Goncharow, Alec; boekelheide, Anna; Mcquaigue, Matthew; Burlinson, David; Saule, Erik; Subramanian, Kalpathi (May 2019, 9th NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar-19))

   The NSF/IEEE-TCPP Parallel and Distributed Computing curriculum guidelines released in 2012 (PDC12) is an effort to bring more parallel computing education to early computer science courses. It has been moderately successful, with the inclusion of some PDC topics in the ACM/IEEE Computer Science curriculum guidelines in 2013 (CS13) and some coverage of topics in early CS courses in some universities in the U.S. and around the world. A reason often cited for the lack of a broader adoption is the difficulty for instructors who are not already knowledgable in PDC topics to learn how to teach those topics and align their learning objectives with early CS courses. There have been attempts at bringing textbook chapters, lecture slides, assignments, and demos to the hands of the instructors of early CS classes. However, the effort required to plow through all the available materials and figure out what is relevant to a particular class is daunting. This paper argues that classifying pedagogical materials against the CS13 guidelines and the PDC12 guidelines can provide the means necessary to reduce the burden of adoption for instructors. In this paper, we present CAR-CS, a system that can be used to categorize pedagogical materials according to well- known and established curricular guidelines and show that CAR-CS can be leveraged 1) by PDC experts to identify topics for which pedagogical material does not exist and that should be developed, 2) by instructors of early CS courses to find materials that are similar to the one that they use but that also cover PDC topics, 3) by instructors to check the topics that a course currently covers and those it does not cover. 

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4. Integrating Parallel and Distributed Computing in Early Computing Classes

   https://doi.org/10.1145/3545947.3569623  

   Ghafoor, Sheikh; Weems, Charles; Sussman, Alan; Vaidyanathan, Ramachandran; Prasad, Sushil (March 2022, ACM)

   Parallel and distributed computing (PDC) has become pervasive in all aspects of computing, and thus it is essential that students include parallelism and distribution in the computational thinking that they apply to problem solving, from the very beginning. Computer science education is still teaching to a 20th century model of algorithmic problem solving. Sequence, branch, and loop are taught in our early courses as the only organizing principles needed for algorithms, and we invest considerable time in showing how best to sequentially process large volumes of data. All computing devices that students use currently have multiple cores as well as a GPU in many cases. Most of their favorite applications use multiple cores and numbers of distributed processors. Often concurrency offers simpler solutions than sequential approaches. Industry is desperate for software engineers who think naturally in terms of exploiting these capabilities, rather than seeing them as an exotic upper-level topic that gets layered over a sequential solution. However, we are still teaching students to solve problems using sequential thinking. In this workshop we overview key PDC concepts and provide examples of how they may naturally be incorporated in early computing classes. We will introduce plugged and unplugged curriculum modules that have been successfully integrated in existing computing classes at multiple institutions. We will highlight the upcoming summer training workshop, for which we have funding to support attendance, as well as other CDER (Center for Parallel and Distributed Computing Curriculum Development and Educational Resources) activities. 

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5. HiPC 2024 WORKSHOP 1: 6th Workshop on Education for High Performance Computing (EduHiPC 2024)

   https://doi.org/10.1109/HiPCW63042.2024.00006  

   Prasad, Sushil (December 2024, IEEE)

   High Performance Computing (HPC) and, in general, Parallel and Distributed Computing (PDC) has become pervasive, from supercomputers and server farms containing multicore CPUs and GPUs, to individual PCs, laptops, and mobile devices. Therefore, it is important for every computing professional (and especially every programmer) to understand how parallelism and distributed computing affect problem solving. It is essential for educators to impart a range of PDC and HPC knowledge and skills at multiple levels within the educational fabric woven by Computer Science (CS), Computer Engineering (CE), and related computational curricula including data science. Companies and laboratories need people with these skills, and, as a result, they are finding that they must now engage in extensive on-the-job training. All the while, rapid changes in hardware platforms, languages, and programming environments increasingly challenge educators to decide what to teach and how to teach it in order to prepare students for careers that are increasingly involving PDC and HPC. EduHiPC aims to provide a forum that brings together academia, industry, government, and non-profit organizations – especially from India, its vicinity, and Asia – for exploring and exchanging experiences and ideas about the inclusion of high-performance, parallel, and distributed computing into undergraduate and graduate curriculum of Computer Science, Computer Engineering, Computational Science, Computational Engineering, and computational courses for STEM and business and other non-STEM disciplines. 

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