IEICE TRANSACTIONS on Information
Boosting the Performance of Interconnection Networks by Selective Data Compression
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.12 pp.2057-2065
- Publication Date
- 2022/12/01
- Publicized
- 2022/07/12
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022PAP0005
- Type of Manuscript
- Special Section PAPER (Special Section on Forefront Computing)
- Category
Authors
Naoya NIWA
Keio University
Hideharu AMANO
Keio University
Michihiro KOIBUCHI
National Institute of Informatics / PRESTO JST
Keyword
Latest Issue
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Naoya NIWA, Hideharu AMANO, Michihiro KOIBUCHI, "Boosting the Performance of Interconnection Networks by Selective Data Compression" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 12, pp. 2057-2065, December 2022, doi: 10.1587/transinf.2022PAP0005.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022PAP0005/_p
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@ARTICLE{e105-d_12_2057,
author={Naoya NIWA, Hideharu AMANO, Michihiro KOIBUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Boosting the Performance of Interconnection Networks by Selective Data Compression},
year={2022},
volume={E105-D},
number={12},
pages={2057-2065},
abstract={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.},
keywords={},
doi={10.1587/transinf.2022PAP0005},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Boosting the Performance of Interconnection Networks by Selective Data Compression
T2 - IEICE TRANSACTIONS on Information
SP - 2057
EP - 2065
AU - Naoya NIWA
AU - Hideharu AMANO
AU - Michihiro KOIBUCHI
PY - 2022
DO - 10.1587/transinf.2022PAP0005
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2022
AB - 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.
ER -
English
