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1. 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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2. 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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3. 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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4. 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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5. Faculty Development Workshops for Integrating PDC in Early Undergraduate Curricula: An Experience Report

   https://doi.org/10.1145/3624062.3624097  

   Ghafoor, Sheikh K.; Brown, David W.; Haynes, Ada; Rogers, Mike (November 2023, Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SCW 2023))

   Parallel and Distributed Computing (PDC) has become pervasive and is now exercised on a variety of platforms. Therefore, understanding how parallelism and distributed computing affect problem solving is important for every computing and engineering professional. However, most students in computer science (CS) and computer engineering (CE) programs are still introduced to computational problem solving using an old model, in which all processing is serial and synchronous, with input and output via text using a terminal interface or a local file system. Teaching a range of PDC knowledge and skills at multiple levels in Computer Science (CS) and related Computing and Engineering curricula is essential. The challenges are significant and numerous. Although some progress has been made in terms of curriculum recommendations and educational resources in computer science, trained faculty, motivation, and inertia are still some of the major impediments to introducing PDC early in computing curricula. The authors of this paper conducted a series of week-long faculty training workshops on the integration of PDC topics in CS1 and CS2 classes, and this paper provides an experience report on the impact and effectiveness of these workshops. Our survey results indicate such faculty development workshops can be effective in gradual inclusion of PDC in early computing curricula. 

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