GPU-Accelerated E-Graphs with CUDA
A research thesis project exploring how e-graph based programming language techniques can be implemented efficiently on GPUs.
- Investigates GPU parallelism for accelerating equality saturation and core e-graph operations.
- Connects programming language theory, compiler optimization, and high-performance computing.
- Focuses on making large-scale program reasoning and transformation more scalable.
