@inproceedings{d8b19e87c4b943bcb043bc8e81bcb5d8,
title = "Architecting an autograder for parallel code",
abstract = "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.",
keywords = "Autograding, Online education",
author = "Razvan Carbunescu and Aditya Devarakonda and James Demmel and Steven Gordon and Jay Alameda and Susan Mehringer",
note = "Copyright: Copyright 2014 Elsevier B.V., All rights reserved.; 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE 2014 ; Conference date: 13-07-2014 Through 18-07-2014",
year = "2014",
doi = "10.1145/2616498.2616571",
language = "English (US)",
isbn = "9781450328937",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the XSEDE 2014 Conference",
address = "United States",
}