# Categorization of Program Regions for Agile Compilation using Machine Learning and Hardware Support

## TL;DR

This paper proposes a machine learning and hardware-assisted approach to categorize program regions, enabling more efficient compilation by selectively applying optimizations, balancing compilation time and code performance.

## Contribution

It introduces a novel method for categorizing program regions to optimize compilation efficiency without sacrificing code performance.

## Key findings

- Improved compilation times with maintained code performance
- Effective categorization of program regions using machine learning
- Potential reduction in compilation resource usage

## Abstract

A compiler processes the code written in a high level language and produces machine executable code. The compiler writers often face the challenge of keeping the compilation times reasonable. That is because aggressive optimization passes which potentially will give rise to high performance are often expensive in terms of running time and memory footprint. Consequently the compiler designers arrive at a compromise where they either simplify the optimization algorithm which may decrease the performance of the produced code, or they will restrict the optimization to the subset of the overall input program in which case large parts of the input application will go un-optimized. The problem we address in this paper is that of keeping the compilation times reasonable, and at the same time optimizing the input program to the fullest extent possible. Consequently, the performance of the produced code will match the performance when all the aggressive optimization passes are applied over the entire input program.