The following projects were carried out by the students in this class over a six-week period. Each student (or team) proposed a project, made changes based on the instructor's feedback on the proposal, presented an informal halfway talk, and gave a final presentation. Each project also produced a final report which are provided below. Some of the reports contain links to source code.
Jack Cashman and Jiaqi Nie, Tiny-QMoE
Robert Hochgraf, Towards Cost-Efficient Autotuners for Machine Learning Systems: Characterizing Autotuning Costs in OpenAI’s Triton
Matthew Nappo, Comco: A MLIR-Based Intermediate Representation for CUDA Kernel Fusion
Leo Sciortino, Simulating NVLink Collective Communication Algorithms using ns-3
Jiwon Chang and Ethan Chen, Efficient ML Training with Dynamic Data Pruning and Model Scaling
Adam Brohl, Translating ML Functions to ISPC