Architecting an autograder for parallel code

Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.) Pub Date : 2014-07-13 DOI:10.1145/2616498.2616571

R. Carbunescu, Aditya Devarakonda, J. Demmel, S. Gordon, Jay Alameda, Susan Mehringer

引用次数: 3

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 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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为并行代码构建自动分级器

随着并行计算的发展并成为计算机科学的重要组成部分，必须开发工具来帮助大型课程的作业评分，特别是近年来大规模开放在线课程(MOOCs)的普及。本文描述了一些与构建并行代码自动分级器相关的一般挑战，并提供了一般建议和示例设计决策，涵盖了所提出的作业。本文探讨了使用这些自动分级器来启用XSEDE 2013和2014并行计算课程的结果和经验，使用SDSC-Trestles, TACC-Stampede和PSC-Blacklight的资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of XSEDE16 : Diversity, Big Data, and Science at Scale : July 17-21, 2016, Intercontinental Miami Hotel, Miami, Florida, USA. Conference on Extreme Science and Engineering Discovery Environment (5th : 2016 : Miami, Fla.)

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