Effective communication between user and scheduler is an important prerequisite to achieving a successful scheuling outcome from both parties' perspectives. In a grid or stand-alone high-performance computing (HPC) enviroment, this communication typically takes the form of a user-provided job script containing essential configuration information, including processors/resources required, a requested runtime, and a priority. Users' requested runtimes are notoriously inaccurate as a predictor of actual runimes. This study examines whether users can improve their runtime estimates if a tangible reward is provided for accuracy. We show that under these conditions, about half of users provide an improved estimate, but there is not a substantial improvement in the overall average accracy. Priority, as implemented in many production scheduers, is a very crude approximation of the value users may attach to timely job completion. We show users are capble of providing richer utility functions than most schedulers elicit. Thus we explore two elements of the user–scheuler dialogue to understand if accuracy and completeness of information conveyed could be improved.

Peer Instruction (PI) is an active learning pedagogical technique. PI lectures present students with a series of multiple-choice questions, which they respond to both individually and in groups. PI has been widely successful in the physical sciences and, recently, has been successfully adopted by computer science instructors in lower-division, introductory courses. In this work, we challenge readers to consider PI for their upper-division courses as well. We present a PI curriculum for two upper-division computer science courses: Computer Architecture and Theory of Computation. These courses exemplify several perceived challenges to the adoption of PI in upper-division courses, including: exploration of abstract ideas, development of high-level judgment of engineering design trade-offs, and exercising advanced mathematical sophistication. This work includes selected course materials illustrating how these challenges are overcome, learning gains results comparing these upper-division courses with previous lower-division results in the literature, student attitudinal survey results (N = 501), and pragmatic advice to prospective developers and adopters. We present three main findings. First, we find that these upper-division courses achieved student learning gains equivalent to those reported in successful lower-division computing courses. Second, we find that student feedback for each class was overwhelmingly positive, with 88% of students recommending PI for use in other computer science classes. Third, we find that instructors adopting the materials introduced here were able to replicate the outcomes of the instructors who developed the materials in terms of student learning gains and student feedback.

Computer system batch schedulers typically require information from the user upon job submission, including a runtime estimate. Inaccuracy of these runtime estimates, relative to the actual runtime of the job, has been well documented and is a perennial problem mentioned in the job scheduling literature. Typically users provide these estimates under circumstances where their job will be killed after the provided amount of time elapses. Also, users may be unaware of the potential benefits of providing accurate estimates, such as increased likelihood of backfilling. This study examines user behavior when the threat of job killing is removed, and when a tangible reward for accuracy is provided. We show that under these conditions, about half of users provide an improved estimate, but there is not a substantial improvement in the overall average accuracy.

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