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https://doi.org/10.1109/ipdpsw...
Article . 2017 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2017
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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I Can Has Supercomputer? A Novel Approach to Teaching Parallel and Distributed Computing Concepts Using a Meme-Based Programming Language

Authors: Richie, David; Ross, James;

I Can Has Supercomputer? A Novel Approach to Teaching Parallel and Distributed Computing Concepts Using a Meme-Based Programming Language

Abstract

A novel approach is presented to teach the parallel and distributed computing concepts of synchronization and remote memory access. The single program multiple data (SPMD) partitioned global address space (PGAS) model presented in this paper uses a procedural programming language appealing to undergraduate students. We propose that the amusing nature of the approach may engender creativity and interest using these concepts later in more sober environments. Specifically, we implement parallel extensions to LOLCODE within a source-to-source compiler sufficient for the development of parallel and distributed algorithms normally implemented using conventional high-performance computing languages and APIs.

7 pages, 2 figures, example code, accepted for publication at the 7th NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar-17) workshop in conjunction with the 31st IEEE International Parallel & Distributed Processing Symposium (IPDPS 17)

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Keywords

FOS: Computer and information sciences, Computer Science - Programming Languages, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), Programming Languages (cs.PL)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green