Jeho Park

Student learning happens everywhere, not just inside the classroom. How can we ensure that students actually learn from extracurricular activities? Digital badges are one solution to this dilemma. To demonstrate digital badges as a means of assessment and microcredentialing tool, we ran a digital badge pilot for our scientific computing workshops. Student feedback and participant data collected throughout the pilot program showed promising results. We will share the practical aspects of preparing and running a digital badge program and propose five things you should know about digital badges for assessing student learning outside the classroom.

Almost all disciplines taught and researched at liberal arts colleges use computer tools and digital data that are neither simpler nor smaller than those used in large universities. However, it is often difficult to find readily available advanced computing resources at liberal arts colleges due to various reasons. For the last two years, the IT department at Harvey Mudd has worked to identify high-performance computing resources outside the college. In this poster, we will introduce the resources that liberal arts colleges can obtain and maintain without spending a large amount of money to provide advanced computing for teaching and research.

We introduce a methodology for the study of the application-level performance of time-sharing parallel jobs on a set of compute nodes in high performance clusters and report our findings. We assume that parallel jobs arriving at a cluster need to share a set of nodes with the jobs of other users, in that they must compete for processor time in a time-sharing manner and other limited resources such as memory and I/O in a space-sharing manner. Under the assumption, we developed a methodology to simulate job arrivals to a set of compute nodes, and gather and process performance data to calculate the percentage slowdown of parallel jobs. Our goal through this study is to identify a better combination of jobs that minimize performance degradations due to resource sharing and contention. Through our experiments, we found a couple of interesting behaviors for overlapped parallel jobs, which may be used to suggest alternative job allocation schemes aiming to reduce slowdowns that will inevitably result due to resource sharing on a high performance computing cluster. We suggest three job allocation strategies based on our empirical results and propose further studies of the results using a supercomputing facility at the San Diego Supercomputing Center.