Welcome to the 4 th Workshop on Education for High Performance Computing (EduHiPC 2022). The EduHiPC 2022 workshop, held in conjunction with the IEEE International Conference on High Performance Computing Data & Analytics (HiPC 2022), is devoted to the development and assessment of educational and curricular innovations and resources for undergraduate and graduate education in Parallel and Distributed Computing (PDC) and High Performance Computing (HPC). EduHiPC brings together individuals from academia, industry, and other educational and research institutes to explore new ideas, challenges, and experiences related to PDC pedagogy and curricula. The workshop is designed in coordination with the IEEE TCPP curriculum initiative on parallel and distributed computing ( hitps://tcpp.cs.gsu .edu/curriculum/) for undergraduates majoring in computer science and computer engineering. It is supported by C-DAC, India and the US National Science Foundation (NSF) supported Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER). Details for attending the workshop are available on the HiPC webpage (HiPC). The effect of pandemic on academic and research community seems now to be globally receding as was evident from the enthusiastic in-person participation of conference delegates. Please visit the EduHiPC-22 webpage for the complete online proceedings, including copies of papers and presentation slides: EduHiPC 2022 | NSF/IEEE-TCPP Curriculum Initiative.
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VSI: Edu*-2016 - Keeping up with technology: Teaching Parallel, Distributed and High-Performance Computing
This special issue is devoted to progress in one of the most important challenges facing computing education.The work published here is of relevance to those who teach computing related topics at all levels, with greatest implications for undergraduate education. Parallel and distributed computing (PDC) has become ubiquitous to the extent that even casual users feel their impact. This necessitates that every programmer understands how parallelism and a distributed environment affect problem solving. Thus,teaching only traditional, sequential programming is no longer adequate. For this reason, it is essential to impart a range of PDC and high performance computing (HPC) knowledge and skills at various levels within the educational fabric woven by Computer Science (CS), Computer Engineering (CE), and related computational science and engineering curricula.
This special issue sought high quality contributions in the fields of PDC and HPC education. Submissions were on the topics of EduPar2016, Euro-EduPar2016 and EduHPC2016 workshops,but the submission was open to all.
This special issue includes 12 paper spanning pedagogical techniques, tools and experiences.
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- Award ID(s):
- 1727395
- NSF-PAR ID:
- 10127598
- Date Published:
- Journal Name:
- Journal of parallel and distributed computing
- Volume:
- 118
- Issue:
- 1
- ISSN:
- 0743-7315
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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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.more » « less
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High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem-solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project.more » « less
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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.more » « less
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
