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. Even casual users of computers now depend on parallel processing. Therefore, it is important for every computer user (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. Nevertheless, 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 likely to involve PDC and HPC.

This workshop invites unpublished manuscripts from academia, industry, and government laboratories on topics pertaining to the needs and approaches for augmenting undergraduate and graduate education in Computer Science and Engineering, Computational Science, and computational courses for both STEM and business disciplines with PDC and HPC concepts. Recently the workshop also extended its focus to data science and computational science education. Additionally, we highly encourage manuscripts that validate their innovative approaches through the systematic collection and analysis of information to evaluate  their performance and impact.

The workshop is particularly dedicated to bringing together stakeholders from industry (both hardware vendors and employers), government labs, and academia in the context of SC-18. The goal is for each to hear the challenges faced by others, to learn about various approaches to addressing these challenges, and to have opportunities to exchange ideas and solutions. In addition to contributed talks, this workshop may feature invited talks on opportunities for collaboration, resource sharing, educator training, internships, and other means of increasing cross-fertilization between industry, government, and academia.


Abstract Submission deadline:Monday, August 20, 2018 (11:59 pm Anywhere on Earth)
Paper Submission deadline:Monday, August 27, 2018 (11:59 pm Anywhere on Earth)
Paper Author notification: Monday, September 24, 2018 
Paper Camera-ready paper deadline: Friday, October 5, 2018
Peachy Assignment submission deadline: Friday September 7, 2018 (11:59 pm Anywhere on Earth)
Peachy Assignment Author notification: Monday, September 24, 2018
Peachy Assignment Camera Ready: Monday October 1, 2018
Early conference registration deadline: Friday October 12, 2018
Workshop: Sunday, November 11, 2018

Call for paper

Important Dates

Draft paper submission deadline:2018-08-27

Topics of submission

Topics of interest include, but are not limited to:

1. Pedagogical issues in incorporating PDC and HPC in undergraduate and graduate education, especially in core courses
2. Novel ways of teaching PDC and HPC topics
3. Data Science and Big Data aspects of teaching HPC/PDC including early experience with data science degree programs.
4. Evidence-based educational practices for teaching HPC/PDC topics that provides evidence about what works best under what circumstances.
5. Experience with incorporating PDC and HPC topics into core CS/CE courses and in domain Computational Science and Engineering courses
6. Pedagogical tools, programming environments, infrastructures, languages, and projects for PDC and HPC
7. Employers' experiences with and expectation of the level of PDC and HPC proficiency among new graduates
8. Education resources based on higher-level programming languages and environments such as X10, Chapel, Haskell, Python, Cilk, CUDA, OpenCL, OpenACC, Hadoop, and Spark.  
9. Parallel and distributed models of programming and computation suitable for teaching, learning, and workforce development.
10. Projects or units that introduce students to concepts relevant to Internet of Things, networking, or other topics in mobile devices or sensor networks.
11. Issues and experiences addressing the gender gap in computing and broadening participation of underrepresented groups.


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Sponsored By

  • Association for Computing Machinery - ACM
    IEEE Computer Society