CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming

TL;DR

CoolerSpace is a Python library that enables physically correct and efficient color programming by using a type system and optimization techniques, outperforming existing systems without runtime overhead.

Contribution

It introduces a new type system and optimization framework for color programming, ensuring physical correctness and computational efficiency.

Findings

Prevents common errors in color programming.

Outperforms existing Python-based systems by up to 5.7 times.

Provides additional speed-up of 1.4 times through optimizations.

Abstract

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 CoolerSpace to facilitate physically correct and computationally efficient color programming. CoolerSpace 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 CoolerSpace. The type system in CoolerSpace 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, CoolerSpace also generates performance-optimized programs using equality saturation. CoolerSpace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. CoolerSpace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized CoolerSpace 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.