No 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 PSCBlacklight.
Learning the principles of computational modeling and parallel computing requires more than a short workshop. Workshops generally run from a few hours to a few days and are therefore limited in the amount of material that can be covered. In addition, it is more difficult for participants to retain large amounts of new material under the time pressures of a workshop. Deeper understanding of such complex materials can come from more traditional academic courses. Yet, many institutions either lack the expertise or the curriculum flexibility to offer such courses. In the spring of 2013 we offered the equivalent of a full semester course entitled Applications of Parallel Computing as an open, online course in an effort to address these issues. The course was offered over a period of thirteen weeks using materials captured from the University of California Berkeley course CS267. Enrollment was initially limited to 345 students. Creating and implementing the course involved decisions in several areas: design of the instructional materials, creating an environment to run programming assignments, support mechanisms for the large number of students taking the course, and automatic grading of assignments. In this session, we will present a summary of the experience in addressing these questions along with an evaluation of the course outcomes.
This paper uses accounting concepts—particularly the concept of Return on Investment (ROI)—to reveal the quantitative value of scientific research pertaining to a major US cyberinfrastructure project (XSEDE—the eXtreme Science and Engineering Discovery Environment). XSEDE provides operational and support services for advanced information technology systems, cloud systems, and supercomputers supporting non-classified US research, with an average budget for XSEDE of US$20M+ per year over the period studied (2014–2021). To assess the financial effectiveness of these services, we calculated a proxy for ROI, and converted quantitative measures of XSEDE service delivery into financial values using costs for service from the US marketplace. We calculated two estimates of ROI: a Conservative Estimate, functioning as a lower bound and using publicly available data for a lower valuation of XSEDE services; and a Best Available Estimate, functioning as a more accurate estimate, but using some unpublished valuation data. Using the largest dataset assembled for analysis of ROI for a cyberinfrastructure project, we found a Conservative Estimate of ROI of 1.87, and a Best Available Estimate of ROI of 3.24. Through accounting methods, we show that XSEDE services offer excellent value to the US government, that the services offered uniquely by XSEDE (that is, not otherwise available for purchase) were the most valuable to the facilitation of US research activities, and that accounting-based concepts hold great value for understanding the mechanisms of scientific research generally.
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