TL;DR
GroupTuner is a novel compiler auto-tuning method that efficiently explores option groups to improve program performance, outperforming existing techniques with less tuning time and better results.
Contribution
It introduces a group-aware auto-tuning approach that avoids explicit critical option identification, enhancing efficiency and effectiveness in compiler optimization.
Findings
Achieves 12.39% average performance improvement over -O3.
Uses only 77.21% of the tuning time compared to random search.
Significantly outperforms state-of-the-art auto-tuning methods.
Abstract
Modern compilers typically provide hundreds of options to optimize program performance, but users often cannot fully leverage them due to the huge number of options. While standard optimization combinations (e.g., -O3) provide reasonable defaults, they often fail to deliver near-peak performance across diverse programs and architectures. To address this challenge, compiler auto-tuning techniques have emerged to automate the discovery of improved option combinations. Existing techniques typically focus on identifying critical options and prioritizing them during the search to improve efficiency. However, due to limited tuning iterations, the resulting data is often sparse and noisy, making it highly challenging to accurately identify critical options. As a result, these algorithms are prone to being trapped in local optima. To address this limitation, we propose GroupTuner, a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
