Publication detail

Optimal Hardware Parameters Prediction for Best Energy-to-Solution of Sparse Matrix Operations Using Machine Learning Techniques

NIKL, V. ŘÍHA, L. VYSOCKÝ, O. ZAPLETAL, J.

Original Title

Optimal Hardware Parameters Prediction for Best Energy-to-Solution of Sparse Matrix Operations Using Machine Learning Techniques

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Combinations of 3 hardware parameters (number of threads, core and uncore frequency) were tested for each of the 4 sparse algorithms (matrix-matrix addition, matrix-matrix multiplication, matrix-vector multiplication in IJV and CSR format) on a set of several thousands matrices for the purpose of identifying the best energy-to-solution setting for each matrix and sparse operation. On this set of data, the possibility of optimal hardware setting prediction based on the properties of each matrix were analysed for each sparse algorithm. A calculation of Pearson correlation coefficient between the matrices' properties and optimal hardware parameters showed no direct correlation (highest 0.33 for x-y, lowest -0.25 for a-b). A neural network with back-propagation learning was used for deeper analysis to see if matrix properties correspond to hardware settings. The input neurons represented properties of given matrix, output neurons represented optimal hardware parameters. Network properties (hidden neurons per layer, hidden neuron layers, learning coefficient and learning strategy) impact on prediction accuracy were analysed and the results showed

Keywords

sparse, neural networks, energy efficiency, prediction

Authors

NIKL, V.; ŘÍHA, L.; VYSOCKÝ, O.; ZAPLETAL, J.

Released

14. 6. 2018

Publisher

International Academy, Research, and Industry Association

Location

Barcelona

ISBN

978-1-61208-655-2

Book

INFOCOMP 2018

Edition

The Eighth International Conference on Advanced Communications and Computation

Pages from

43

Pages to

48

Pages count

6

URL

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&ved=2ahUKEwj45dbYkMfdAhUilYsKHQe1DkwQFjADegQIBxAC&url=https%3A%2F%2Fwww.thinkmind.org%2Fdownload_full.php%3Finstance%3DINFOCOMP%2B2018&usg=AOvVaw0F5eFy3SoDGqt3wTWnO1GV

BibTex

@inproceedings{BUT155017,
  author="Vojtěch {Nikl} and Lubomír {Říha} and Ondřej {Vysocký} and Jan {Zapletal}",
  title="Optimal Hardware Parameters Prediction for Best Energy-to-Solution of Sparse Matrix Operations Using Machine Learning Techniques",
  booktitle="INFOCOMP 2018",
  year="2018",
  series="The Eighth International Conference on Advanced Communications and Computation",
  pages="43--48",
  publisher="International Academy, Research, and Industry Association",
  address="Barcelona",
  isbn="978-1-61208-655-2",
  url="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&ved=2ahUKEwj45dbYkMfdAhUilYsKHQe1DkwQFjADegQIBxAC&url=https%3A%2F%2Fwww.thinkmind.org%2Fdownload_full.php%3Finstance%3DINFOCOMP%2B2018&usg=AOvVaw0F5eFy3SoDGqt3wTWnO1GV"
}