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Title: Exploring Deep Learning and Sparse Matrix Format Selection

Abstract

We proposed to explore the use of Deep Neural Networks (DNN) for addressing the longstanding barriers. The recent rapid progress of DNN technology has created a large impact in many fields, which has significantly improved the prediction accuracy over traditional machine learning techniques in image classifications, speech recognitions, machine translations, and so on. To some degree, these tasks resemble the decision makings in many HPC tasks, including the aforementioned format selection for SpMV and linear solver selection. For instance, sparse matrix format selection is akin to image classification—such as, to tell whether an image contains a dog or a cat; in both problems, the right decisions are primarily determined by the spatial patterns of the elements in an input. For image classification, the patterns are of pixels, and for sparse matrix format selection, they are of non-zero elements. DNN could be naturally applied if we regard a sparse matrix as an image and the format selection or solver selection as classification problems.

Authors:
 [1];  [1];  [2]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. North Carolina State Univ., Raleigh, NC (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1426119
Report Number(s):
LLNL-SR-747311
DOE Contract Number:
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Zhao, Y., Liao, C., and Shen, X.. Exploring Deep Learning and Sparse Matrix Format Selection. United States: N. p., 2018. Web. doi:10.2172/1426119.
Zhao, Y., Liao, C., & Shen, X.. Exploring Deep Learning and Sparse Matrix Format Selection. United States. doi:10.2172/1426119.
Zhao, Y., Liao, C., and Shen, X.. Tue . "Exploring Deep Learning and Sparse Matrix Format Selection". United States. doi:10.2172/1426119. https://www.osti.gov/servlets/purl/1426119.
@article{osti_1426119,
title = {Exploring Deep Learning and Sparse Matrix Format Selection},
author = {Zhao, Y. and Liao, C. and Shen, X.},
abstractNote = {We proposed to explore the use of Deep Neural Networks (DNN) for addressing the longstanding barriers. The recent rapid progress of DNN technology has created a large impact in many fields, which has significantly improved the prediction accuracy over traditional machine learning techniques in image classifications, speech recognitions, machine translations, and so on. To some degree, these tasks resemble the decision makings in many HPC tasks, including the aforementioned format selection for SpMV and linear solver selection. For instance, sparse matrix format selection is akin to image classification—such as, to tell whether an image contains a dog or a cat; in both problems, the right decisions are primarily determined by the spatial patterns of the elements in an input. For image classification, the patterns are of pixels, and for sparse matrix format selection, they are of non-zero elements. DNN could be naturally applied if we regard a sparse matrix as an image and the format selection or solver selection as classification problems.},
doi = {10.2172/1426119},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Mar 06 00:00:00 EST 2018},
month = {Tue Mar 06 00:00:00 EST 2018}
}

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