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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. 

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. 

The ACM/IEEE CS 2013 report recommends fifteen hours of parallel & distributed computing (PDC) education for every undergraduate. This workshop illustrates the use of the Raspberry Pi as an inexpensive, multicore platform for teaching shared-memory parallel programming. The inexpensive and tactile nature of the Raspberry Pi enables each student to experience her own parallel multiprocessor through sight and touch. In this hands-on workshop, we will teach attendees how they can leverage the Raspberry Pi and the OpenMP library to teach shared-memory parallel concepts in their own classrooms. All CS educators who are interested in learning about the Raspberry Pi, shared memory parallelism, and OpenMP are encouraged to attend. In Part I of the workshop, each participant will connect to and learn about the Raspberry Pi's multicore capabilities. In Part II, each participant will engage in self-paced, hands-on exploration of basic parallel computing concepts using the OpenMP "patternlets" from CSinParallel.org. In Part III, participants will investigate more complex applications, such as numeric integration and drug design and study how these applications can be parallelized using OpenMP. We will conclude the workshop with a series of lightning talks discussing how the Raspberry Pi has been used to teach parallel computing concepts at different institutions. We will also present a summary of student perceptions of the Raspberry Pi. All materials from this workshop will be freely available from CSinParallel.org. Space is limited to 20 participants. A laptop is required.