Boosting the Performance of Interconnection Networks by Selective Data Compression

Naoya Niwa, Hideharu Amano, Michihiro Koibuchi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This study presents a selective data-compression interconnection network to boost its performance. Data compression virtually increases the effective network bandwidth. One drawback of data compression is a long latency to perform (de-)compression operation at a compute node. In terms of the communication latency, we explore the trade-off between the compression latency overhead and the reduced injection latency by shortening the packet length by compression algorithms. As a result, we present to selectively apply a compression technique to a packet. We perform a compression operation to long packets and it is also taken when network congestion is detected at a source compute node. Through a cycle-accurate network simulation, the selective compression method using the above compression algorithms improves by up to 39% the network throughput with a moderate increase in the communication latency of short packets.

Original languageEnglish
Pages (from-to)2057-2065
Number of pages9
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - 2022 Dec


  • communication latency
  • data compression
  • interconnection networks
  • parallel processing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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