ACPO: AI-Enabled Compiler-Driven Program Optimization

no code implementations • 15 Dec 2023 • Amir H. Ashouri, Muhammad Asif Manzoor, Duc Minh Vu, Raymond Zhang, Ziwen Wang, Angel Zhang, Bryan Chan, Tomasz S. Czajkowski, Yaoqing Gao

The key to performance optimization of a program is to decide correctly when a certain transformation should be applied by a compiler.

MLGOPerf: An ML Guided Inliner to Optimize Performance

no code implementations • 18 Jul 2022 • Amir H. Ashouri, Mostafa Elhoushi, Yuzhe Hua, Xiang Wang, Muhammad Asif Manzoor, Bryan Chan, Yaoqing Gao

This paper presents MLGOPerf; the first end-to-end framework capable of optimizing performance using LLVM's ML-Inliner.

reinforcement-learning Reinforcement Learning (RL)

Fast On-the-fly Retraining-free Sparsification of Convolutional Neural Networks

no code implementations • NIPS Workshop CDNNRIA 2018 • Amir H. Ashouri, Tarek S. Abdelrahman, Alwyn Dos Remedios

Our methods are applied on-the-fly and require no retraining.

A Survey on Compiler Autotuning using Machine Learning

no code implementations • 13 Jan 2018 • Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, Cristina Silvano

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems.

BIG-bench Machine Learning Compiler Optimization