This innovative practice full paper introduces a tool for clustering Entity Relationship Diagrams (ERDs) and explores its application in large classes. ERDs are fundamental for database design in courses related to databases, data science, and software engineering. However, processing ERD homework submissions in large classes poses significant challenges due to the variety of design decisions made by students, leading to numerous diagram variations. This paper presents an ERD clustering tool designed to group similar ERD submissions, aiding instructors and teaching assistants in identifying popular solutions and common mistakes. The tool employs advanced object detection, OCR, and clustering technologies. We evaluated the tool using four datasets from two large public U.S. universities, with submissions ranging from 130 to 430 diagrams. Various clustering methodologies were assessed, highlighting the importance of incorporating ERD structure into the clustering process. Our findings indicate that the tool successfully generated adequate clusters, and that aiming for 10 clusters is appropriate regardless of the dataset size. The generated clusters included common approaches and mistakes, proving helpful for providing feedback and simplifying the grading process.

CXL interface is the up-to-date technology that enables effective memory expansion by providing a memory-sharing protocol in configuring heterogeneous devices. However, its limited physical bandwidth can be a significant bottleneck for emerging data-intensive applications. In this work, we propose a novel CXL-based memory disaggregation architecture with a real-world prototype demonstration, which overcomes the bandwidth limitation of the CXL interface using near-data processing. The experimental results demonstrate that our design achieves up to 1.9× better performance/power efficiency than the existing CPU system.