論文

査読有り
2016年

Stream-based lossless data compression hardware using adaptive frequency table management

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Shinichi Yamagiwa
  • ,
  • Koichi Marumo
  • ,
  • Hiroshi Sakamoto

9495
開始ページ
133
終了ページ
146
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1007/978-3-319-29006-5_11
出版者・発行元
Springer Verlag

In order to treat BigData efficiently, the communication speed of the inter or the intra data path equipped on high performance computing systems that needs to treat BigData management has been reaching to very high speed. In spite of fast increasing of the BigData, the implementation of the data communication path has become complex due to the electrical difficulties such as noises, crosstalks and reflections of the high speed data connection via a single cupper-based physical wire. This paper proposes a novel hardware solution to implement it by applying a stream-based data compression algorithm called the LCADLT. The compression algorithm is able to treat continuous data stream without exchanging the symbol lookup table among the compressor and the decompressor. The algorithm includes a dynamic frequency management of data patterns. The management is implemented by a dynamic histogram creation optimized for hardware implementation. When the dedicated communication protocol is combined with the LCA-DLT, it supports remote data migration among the computing systems. This paper describes the algorithm design and its hardware implementation of the LCA-DLT, and also shows the compression performance including the required hardware resources.

リンク情報
DOI
https://doi.org/10.1007/978-3-319-29006-5_11
ID情報
  • DOI : 10.1007/978-3-319-29006-5_11
  • ISSN : 1611-3349
  • ISSN : 0302-9743
  • SCOPUS ID : 84958062578

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