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1. Teaching Parallel and Distributed Computing Concepts Using OpenMPI and Java

   https://doi.org/10.1109/HiPCW54834.2021.00008  

   Adams, Joel C. (December 2021, 3rd Workshop on Education for High Performance Computing (EduHiPC 2021), at 2021 IEEE 28th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW))

   Ghafoor, Sheikh; Prasad, Sushil K. (Ed.)

   The ACM/IEEE CS 2013 curriculum recommendations state that every undergraduate CS major should learn about parallel and distributed computing (PDC). One way to accomplish this is to teach students about the Message Passing Interface (MPI), a platform that is commonly used on modern supercomputers and Beowulf clusters, but can also be used on a Network of Workstations (NoW), or a multicore laptop or desktop. MPI incorporates many PDC concepts and can serve as a platform for hands-on learning activities in which students must apply those concepts. The MPI standard defines language bindings for Fortran and C/C++, but many university instructors lack expertise in these languages, preventing them from using MPI in their courses. OpenMPI is a free implementation of the MPI standard that also provides Java bindings for MPI. This paper describes how to install OpenMPI with these Java bindings; to illustrate the use of these bindings, the paper also presents several patternlets—minimalist example programs—that show how to implement PDC design patterns using OpenMPI and Java. This provides a new means of introducing students to PDC concepts. 

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2. Interactive Textbooks for Parallel and Distributed Computing Across the Undergraduate CS Curriculum

   https://doi.org/10.1109/IPDPSW63119.2024.00085  

   Shoop, Elizabeth; Brown, Richard; Matthews, Suzanne J; Adams, Joel C (May 2024, IEEE)

   It has been a decade since the ACM/IEEE CS2013 Curriculum guidelines recommended that all CS students learn about parallel and distributed computing (PDC). But few text- books for “core” CS courses–especially first-year courses–include coverage of PDC topics. To fill this gap, we have written free, online, beginner- and intermediate-level PDC textbooks, containing interactive C/C++ OpenMP, MPI, mpi4py, CUDA, and OpenACC code examples that students can run and modify directly in the browser. The books address a serious challenge to teaching PDC concepts, namely, easy access to the powerful hardware needed for observing patterns and scalability. This paper describes the content of these textbooks and the underlying infrastructure that make them possible. We believe the described textbooks fill a critical gap in PDC education and will be very useful for the community. 

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3. Introducing Beginners to Distributed Computing using Raspberry Pi Clusters

   https://doi.org/10.1145/3328778.3367004  

   Shoop, Elizabeth; Adams, Joel C.; Brown, Richard; Matthews, Suzanne J. (February 2020, SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education)

   null (Ed.)

   The 2019 ABET computer science criteria requires that all computing students learn parallel and distributed computing (PDC) as undergraduates, and CS2013 recommends at least fifteen hours of PDC in the undergraduate curriculum. Consequently, many educators look for easy ways to integrate PDC into courses at their institutions. This hands-on workshop introduces Message Passing Interface (MPI) basics in C/C++ and Python using clusters of Raspberry Pis. The Message Passing Interface (MPI) is a multi-language, platform independent, industry-standard library for parallel and distributed computing. Raspberry Pis are an inexpensive and engaging hardware platform for studying PDC as early as the first course. Participants will experience how to teach distributed computing essentials with MPI by means of reusable, effective "parallel patterns", including single program multiple data (SPMD) execution, send-receive message passing, the master-worker pattern, parallel loop patterns, and other common patterns, plus longer "exemplar" programs that use MPI to solve significant applied problems. The workshop includes: (i) personal experience with the Raspberry Pi (clusters provided for workshop use); (ii) assembly of Beowulf clusters of Raspberry Pis quickly in the classroom; (iii) self-paced hands-on experimentation with the working MPI programs; and (iv) a discussion of how these may be used to achieve the goals of CS2013 and ABET. No prior experience with MPI, PDC, or the Raspberry Pi is expected. All materials from this workshop will be freely available from CSinParallel.org; participants should bring a laptop to access these materials. 

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4. 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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5. Incorporating Parallel Computing in the Undergraduate Computer Science Curriculum

   https://doi.org/10.1145/3328778.3372511  

   Matthews, Suzanne J.; Adams, Joel C.; Brown, Richard; Shoop, Elizabeth (February 2020, SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education)

   null (Ed.)

   Teaching parallel and distributed computing (PDC) concepts is an ongoing and pressing concern for many undergraduate educators. The ACM/IEEE CS Joint Task Force on Computing Curricula (CS2013) recommends 15 hours of PDC education in the undergraduate curriculum. Most recently, the 2019 ABET Criteria for Accrediting Computer Science requires coverage of PDC topics. For faculty who are unfamiliar with PDC, the prospect of incorporating parallel computing into their courses can seem very daunting. For example, should PDC concepts be covered in a single required course (perhaps computer systems) or be scattered throughout different courses in the undergraduate curriculum? What languages are the best/easiest for students to learn PDC? How much revision is truly needed? This Birds of a Feather session provides a platform for computing educators to discuss the common challenges they face when attempting to incorporate PDC into their curricula and share potential solutions. Chiefly, the organizers are interested in identifying "gap areas" that hinder a faculty member's ability to integrate PDC into their undergraduate courses. The multiple viewpoints and expertise provided by the BOF leaders should lead to lively discourse and enable experienced faculty to share their strategies with those beginning to add PDC across their curricula. We anticipate that this session will be of interest to all CS faculty looking to integrate PDC into their courses and curricula. 

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