OpenTensor: Reproducing Faster Matrix Multiplication Discovering Algorithms

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Sun, Yiwen, Li, Wenye
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866914820549246976
author Sun, Yiwen
Li, Wenye
author_facet Sun, Yiwen
Li, Wenye
contents OpenTensor is a reproduction of AlphaTensor, which discovered a new algorithm that outperforms the state-of-the-art methods for matrix multiplication by Deep Reinforcement Learning (DRL). While AlphaTensor provides a promising framework for solving scientific problems, it is really hard to reproduce due to the massive tricks and lack of source codes. In this paper, we clean up the algorithm pipeline, clarify the technical details, and make some improvements to the training process. Computational results show that OpenTensor can successfully find efficient matrix multiplication algorithms.
format Preprint
id arxiv_https___arxiv_org_abs_2405_20748
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle OpenTensor: Reproducing Faster Matrix Multiplication Discovering Algorithms
Sun, Yiwen
Li, Wenye
Artificial Intelligence
Data Structures and Algorithms
Machine Learning
OpenTensor is a reproduction of AlphaTensor, which discovered a new algorithm that outperforms the state-of-the-art methods for matrix multiplication by Deep Reinforcement Learning (DRL). While AlphaTensor provides a promising framework for solving scientific problems, it is really hard to reproduce due to the massive tricks and lack of source codes. In this paper, we clean up the algorithm pipeline, clarify the technical details, and make some improvements to the training process. Computational results show that OpenTensor can successfully find efficient matrix multiplication algorithms.
title OpenTensor: Reproducing Faster Matrix Multiplication Discovering Algorithms
topic Artificial Intelligence
Data Structures and Algorithms
Machine Learning
url https://arxiv.org/abs/2405.20748