R. Carbunescu, Aditya Devarakonda, J. Demmel, S. Gordon, Jay Alameda, Susan Mehringer
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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 recent years. This paper describes some of the general challenges related to building an autograder for parallel code with general suggestions and sample design decisions covering 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.
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