Production high-performance computing (HPC) systems are adopting and integrating GPUs into their design to accommodate artificial intelligence (AI), machine learning, and data visualization workloads. To aid with the design and operations of new and existing GPU-based large-scale systems, we provide a detailed characterization of system operations, job characteristics, user behavior, and trends on a contemporary GPU-accelerated production HPC system. Our insights indicate that the pre-mature phases in modern AI workflow take up significant GPU hours while underutilizing GPUs, which opens up the opportunity for a multi-tier system. Finally, we provide various potential recommendations and areas for future investment for system architects, operators, and users.

This Innovative Practices Work in Progress describes how serious games can be used to help teach High Performance Computing (HPC). Serious games provide pathways for learners to develop intuition while learning new concepts. We define serious games as those that incorporate learning objectives, educational content, and assessment and whose purpose is learning rather than entertaining. Such games are valuable in an educational context because they engage students in active learning and help students develop mental models from their experiences. These student experiences generally lead to deeper questions, allowing instructors the chance to clarify misunderstandings, and reinforce learning. This work describes two games used in informal HPC courses to provide students with tangible hands-on experiences with HPC concepts.

This Work-In-Progress paper details the process and lessons learned when converting a hands-on engineering mini-course to a scalable, self-paced Massively Open Online Course (MOOC). Online courseware has been part of academic and industry training and learning for decades. Learning activities in online courses strive to mimic in-person delivery by including lectures, homework assignments, software exercises and exams. While these instructional activities provide “theory and practice” for many disciplines, engineering courses often require hands-on activities with physical tools, devices and equipment. To accommodate the need for this type of learning, MIT Lincoln Laboratory's “Build A Small Radar” (BSR) course was used to explore teaching and learning strategies that support the inclusion of physical construction and real world data collection in a MOOC. These tasks are encountered across a range of engineering disciplines and the methods illustrated here are easily generalized to the learning experiences in engineering and science disciplines.