The Cost of Dynamism in Static Languages for Image Processing

Conference paper

21st International Conference on Generative Programming: Concepts & Experiences

Dec 5, 2022 — Dec 10, 2022

University of Auckland

Auckland,

Generic programming is a powerful paradigm abstracting data structures and algorithms to improve their reusability as long as they respect agiven interface. Coupled with a performance-driven language, it is a paradigm of choice for scientific libraries where the implementation of manipulated objects may change in function of their use case or for performance purposes. In those performance-driven languages, genericity is often implemented statically to perform some optimization at compile time. This does not fit well with the dynamism needed to handle objects which may only be known at runtime. Thus, in this article, we evaluate a model that couples static genericity with a dynamic model based on type erasure in the context of image processing. Its cost is assessed by comparing the performance of the implementation of some image processing algorithms in C++ and Rust, two performance-driven languages supporting the genericity paradigm. We finally show that the knowledge of some information at compile time is more important than others, but also that the runtime overhead depends on the algorithmic scheme.