Optimized Polynomial Evaluation with Semantic Annotations

TL;DR

This paper explores how semantic annotations can enhance polynomial evaluation algorithms, enabling compilers to optimize code for better performance, portability, and maintainability across diverse hardware architectures.

Contribution

It introduces a novel approach using semantic annotations to embed mathematical algorithmic information into code, improving compiler optimizations for polynomial evaluations.

Findings

Enhanced performance through semantic annotations

Improved code portability across architectures

Better programmability and maintainability

Abstract

In this paper we discuss how semantic annotations can be used to introduce mathematical algorithmic information of the underlying imperative code to enable compilers to produce code transformations that will enable better performance. By using this approaches not only good performance is achieved, but also better programmability, maintainability and portability across different hardware architectures. To exemplify this we will use polynomial equations of different degrees.