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<title>WebGPU: A Scalable Online Development Platform for GPU Programming Courses | Carl Pearson</title>
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<h1 itemprop="name">WebGPU: A Scalable Online Development Platform for GPU Programming Courses</h1>
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Adbul Dakkak, Carl Pearson, Cheng Li
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<h3>Abstract</h3>
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<p class="pub-abstract" itemprop="text">The popularity of computer science classes offered through Massive Open On-line Courses (MOOCs) creates both opportunities and challenges.
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Programming-based classes need to provide consistent development infrastructures that are both scalable and user friendly to students.
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The \"Heterogeneous Parallel Programming\" class offered through Coursera teaches GPU programming and encountered these problems.
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We developed WebGPU - an online GPU development platform - providing students with a user friendly scalable GPU computing platform throughout the course.
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It has been used as the CUDA, OpenACC, and OpenCL programming environment for large Coursera courses, short-running summer schools, and traditional semester-long graduate and undergraduate courses.
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WebGPU has since replaced our traditional development infrastructure for the GPU classes offered at UIUC.
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This paper presents the original, revised, and upcoming WebGPU designs that address the requirements and challenges of offering sophisticated computing resources to a large, quickly-varying number of students.</p>
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<div class="col-xs-12 col-sm-3 pub-row-heading">Publication</div>
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<div class="col-xs-12 col-sm-9"><em>Parallel and Distributed Processing Symposium Workshops, 2016 IEEE International.</em> IEEE, 2016.</div>
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<div class="col-xs-12 col-sm-3 pub-row-heading">Date</div>
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<div class="col-xs-12 col-sm-9" itemprop="datePublished">
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January, 2016
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aria-hidden="true">←</span> Adaptive Cache Bypass and Insertion for Many-Core Accelerators</a></li>
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© 2017 Carl Pearson ·
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