Architecting an autograder for parallel code

Razvan Carbunescu, Aditya Devarakonda, James Demmel, Steven Gordon, Jay Alameda, Susan Mehringer

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As parallel computing grows and becomes an essential part of computer science, tools must be developed to help grade assignments for large courses, especially with the prevalence of Massive Open Online Courses (MOOCs) increasing in re-cent years. This paper describes some of the general chal-lenges related to building an autograder for parallel code with general suggestions and sample design decisions cover-ing presented assignments. The paper explores the results and experiences from using these autograders to enable the XSEDE 2013 and 2014 Parallel Computing Course using resources from SDSC-Trestles, TACC-Stampede and PSC-Blacklight.

Original languageEnglish (US)
Title of host publicationProceedings of the XSEDE 2014 Conference
Subtitle of host publicationEngaging Communities
PublisherAssociation for Computing Machinery
ISBN (Print)9781450328937
StatePublished - 2014
Event2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014 - Atlanta, GA, United States
Duration: Jul 13 2014Jul 18 2014

Publication series

NameACM International Conference Proceeding Series


Other2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014
Country/TerritoryUnited States
CityAtlanta, GA


  • Autograding
  • Online education

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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