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MOARLE: Matrix Operation Accelerator Based on Run-Length Encoding

  • Masafumi Oyamada
  • Jianquan Liu
  • Kazuyo Narita
  • Takuya Araki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8709)

Abstract

Matrix computation is a key technology in various data processing tasks including data mining, machine learning, and information retrieval. Size of matrices has been increasing with the development of computational resources and dissemination of big data. Huge matrices are memory- and computational-time-consuming. Therefore, reducing the size and computational time of huge matrices is a key challenge in the data processing area. We develop MOARLE, a novel matrix computation framework that saves memory space and computational time. In contrast to conventional matrix computational methods that target to sparse matrices, MOARLE can efficiently handle both sparse matrices and dense matrices. Our experimental results show that MOARLE can reduce the memory usage to 2% of the original usage and improve the computational performance by a factor of 124x.

Keywords

Matrix Compression Run-length encoding Similarity search Euclidean distance 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Masafumi Oyamada
    • 1
  • Jianquan Liu
    • 1
  • Kazuyo Narita
    • 1
  • Takuya Araki
    • 1
  1. 1.Green Platform Research Labs., NEC Corp.KawasakiJapan

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