Using Deep Neural Networks for Estimating Loop Unrolling Factor

TL;DR

This paper presents a deep neural network model to automatically predict the optimal loop unrolling factor in TIRAMISU, a polyhedral framework for high-performance code generation across diverse hardware platforms.

Contribution

It introduces a novel deep learning approach to automate loop unrolling optimization within TIRAMISU, reducing manual tuning efforts and improving program performance.

Findings

The neural network accurately predicts unrolling factors for TIRAMISU programs.

Automated predictions lead to performance improvements over manual tuning.

The approach generalizes across multiple hardware platforms.

Abstract

Optimizing programs requires deep expertise. On one hand, it is a tedious task, because it requires a lot of tests to find out the best combination of optimizations to apply with their best factors. On the other hand, this task is critical, because it may degrade the performance of programs instead of improving it. The automatization of this task can deal with this problem and permit to obtain good results. Optimizing loops that take the most significant part of the program execution time plays a crucial role to achieve best performance. In this paper, we address Loop unrolling optimization, by proposing a deep Neural Network model to predict the optimal unrolling factor for programs written for TIRAMISU. TIRAMISU is a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. TIRAMISU introduces a scheduling language with novel commands to explicitly manage the complexities that arise when targeting these systems.