Summary of Explore As a Storm, Exploit As a Raindrop: on the Benefit Of Fine-tuning Kernel Schedulers with Coordinate Descent, by Michael Canesche et al.
Explore as a Storm, Exploit as a Raindrop: On the Benefit of Fine-Tuning Kernel Schedulers with Coordinate Descent
by Michael Canesche, Gaurav Verma, Fernando Magno Quintao Pereira
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators can understand that this paper presents a novel approach to kernel scheduling, where kernel optimizers like Ansor and Halide use search heuristics to find the best implementation of a kernel. The study combines AutoTVM’s Droplet Search algorithm with Ansor’s exploration phase to reduce search time while enhancing kernel quality. This is achieved by limiting the number of samples explored, selecting the best, and exploiting it with a coordinate descent algorithm. The paper demonstrates that this approach can lead to better kernels in less time, replicating results on 20 well-known deep-learning models running on four architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes machine learning models more efficient! It’s about finding the right way to combine different parts of an algorithm (called a kernel) to make it work faster. The researchers used special algorithms to search for the best combination and found that it works really well. They even tested it on lots of popular AI models and saw that it makes them run faster too! |
Keywords
» Artificial intelligence » Deep learning » Machine learning
