Zina Taklit

Asma Balamane, Zina Taklit

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.

Asma Balamane, Zina Taklit

Computer architectures become more and more complex. It requires more effort to develop techniques that improve the programs of performance and allow to exploit material resources efficiently. As a result, many transformations are applied on various levels of code abstraction. The first level is the high level, where the representation is close to the high level language. The second one is the low level, where the presentation is close to the machine code. Those transformations are called code optimizations. 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 the program instead of improving it. The automatization of this task can deal with this problem and permit to obtain good results. Our end of study project consists on proposing a novel approach based on neural networks to automatically optimize loops in Tiramisu. Tiramisu is a new language to create a code of high performance. It allows to separate between the algorithm and its optimizations. We have chosen loop unrolling as a study case. Our contribution aims to automate the choice of the best loop unrolling factor for a program written in Tiramisu.