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It is typical in High Performance Computing (HPC) courses to give students access to HPC platforms so that they can benefit from hands-on learning opportunities. Using such platforms, however, comes with logistical and pedagogical challenges. For instance, a logistical challenge is that access to representative platforms must be granted to students, which can be difficult for some institutions or course modalities; and a pedagogical challenge is that hands-on learning opportunities are constrained by the configurations of these platforms. A way to address these challenges is to instead simulate program executions on arbitrary HPC platform configurations. In this work we focus on simulation in the specific context of distributed-memory computing and MPI programming education. While using simulation in this context has been explored in previous works, our approach offers two crucial advantages. First, students write standard MPI programs and can both debug and analyze the performance of their programs in simulation mode. Second, large-scale executions can be simulated in short amounts of time on a single standard laptop computer. This is possible thanks to SMPI, an MPI simulator provided as part of SimGrid. After detailing the challenges involved when using HPC platforms for HPC education and providing background information about SMPI, we present SMPI Courseware. SMPI Courseware is a set of in-simulation assignments that can be incorporated into HPC courses to provide students with hands-on experience for distributed-memory computing and MPI programming learning objectives. We describe some these assignments, highlighting how simulation with SMPI enhances the student learning experience.
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Collective Development of Large Scale Data Science Products via Modularized Assignments: An Experience Report
Many universities are offering data science (DS) courses to fulfill the growing demands for skilled DS practitioners. Assignments and projects are essential parts of the DS curriculum as they enable students to gain hands-on experience in real-world DS tasks. However, most current assignments and projects are lacking in at least one of two ways: 1) they do not comprehensively teach all the steps involved in the complete workflow of DS projects; 2) students work on separate problems individually or in small teams, limiting the scale and impact of their solutions. To overcome these limitations, we envision novel synergistic modular assignments where a large number of students work collectively on all the tasks required to develop a large-scale DS product. The resulting product can be continuously improved with students' contributions every semester. We report our experience with developing and deploying such an assignment in an Information Retrieval course. Through the assignment, students collectively developed a search engine for finding expert faculty specializing in a given field. This shows the utility of such assignments both for teaching useful DS skills and driving innovation and research. We share useful lessons for other instructors to adopt similar assignments for their DS courses.
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- Award ID(s):
- 1801652
- PAR ID:
- 10172972
- Date Published:
- Journal Name:
- Proceedings of the 51st ACM Technical Symposium on Computer Science Education
- Page Range / eLocation ID:
- 1200–1206
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3328778.3366961
