Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce C ooler S pace to facilitate physically correct and computationally efficient color programming. C ooler S pace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by C ooler S pace . The type system in C ooler S pace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, C ooler S pace also generates performance-optimized programs using equality saturation. C ooler S pace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. C ooler S pace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized C ooler S pace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.

@article{ChenC024,
  title = {CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming},
  author = {Ethan Chen and Jiwon Chang and Yuhao Zhu},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {8},
  number = {OOPSLA2},
  year = {2024},
  doi = {10.1145/3689741}
}