Using Inexpensive Microclusters and Accessible Materials for Cost-Effective Parallel and Distributed Computing Education

dc.contributor.authorAdams, Joel C.
dc.contributor.authorMatthews, Suzanne J.
dc.contributor.authorShoop, Elizabeth
dc.contributor.authorToth, David
dc.contributor.authorWolfer, James
dc.date.accessioned2023-11-07T19:16:38Z
dc.date.available2023-11-07T19:16:38Z
dc.date.issued2017-12
dc.description.abstractWith parallel and distributed computing (PDC) now in the core CS curriculum, CS educators are building new pedagogical tools to teach their students about this cutting-edge area of computing. In this paper, we present an innovative approach we call microclusters – personal, portable Beowulf clusters – that provide students with hands-on PDC learning experiences. We present several different microclusters, each built using a different combination of single board comput ers (SBCs) as its compute nodes, including various ODROID models, Nvidia’s Jetson TK1, Adapteva’s Parallella, and the Raspberry Pi. We explore different ways that CS educators are using these systems in their teaching, and describe specific courses in which CS educators have used microclusters. Finally, we present an overview of sources of free PDC pedagogical materials that can be used with microclusters.
dc.description.sponsorshipDepartment of Electrical Engineering and Computer Science
dc.identifier.citationAdams et al., “Using Inexpensive Microclusters and Accessible Materials for Cost-Effective Parallel and Distributed Computing Education.”
dc.identifier.doihttps://doi.org/10.22369/issn.2153-4136/8/3/1
dc.identifier.issn2153-4136
dc.identifier.urihttps://hdl.handle.net/20.500.14216/1176
dc.publisherThe Shodor Education Foundation, Inc.
dc.relation.ispartofThe Journal of Computational Science Education
dc.subjectBeowulf clusters
dc.subjectmicrocluster
dc.subjectComputer Science
dc.subjectDistributed Parallel
dc.subjectEducation
dc.subjectTeaching
dc.titleUsing Inexpensive Microclusters and Accessible Materials for Cost-Effective Parallel and Distributed Computing Education
dc.typejournal-article
local.peerReviewedYes
oaire.citation.issue3
oaire.citation.volume8

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