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Summary of Optimal Kernel Tuning Parameter Prediction Using Deep Sequence Models, by Khawir Mahmood et al.


Optimal Kernel Tuning Parameter Prediction using Deep Sequence Models

by Khawir Mahmood, Jehandad Khan, Hammad Afzal

First submitted to arxiv on: 15 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

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GrooveSquid.com Paper Summaries

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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
This paper proposes a novel methodology that leverages deep sequence-to-sequence models from Natural Language Processing (NLP) to predict the optimal tuning parameters for GPU compute kernels. The authors frame kernel parameter prediction as a sequence-to-sequence translation problem, where input tensors are translated into corresponding kernel parameters. The proposed algorithm, which incorporates physical limits of the GPU hardware and expert knowledge, achieves over 90% accuracy on various convolutional kernels in MIOpen, the AMD machine learning primitives library. This technique has significant implications for reducing development time, compute resources, and costs, ultimately leading to a better user experience.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about finding the best way to make computer chips (GPUs) work efficiently. Right now, it takes a lot of trial and error to get them running well. The authors came up with a new way to use special kinds of language models to figure out the right settings for the GPUs. This could help people develop new programs faster and use fewer resources. It’s like having a superpower that helps you solve puzzles and make computers work better.

Keywords

» Artificial intelligence  » Machine learning  » Natural language processing  » Nlp  » Translation