Improved data transfer efficiency for scale‐out heterogeneous workloads using on‐the‐fly I/O link compression-Reference-Cited by-同舟云学术

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Improved data transfer efficiency for scale‐out heterogeneous workloads using on‐the‐fly I/O link compression

Published:2020-12 Issue: Volume: Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Plauth Max¹^ORCID,Bruguera Micó Joan²,Polze Andreas¹

Affiliation:

1. Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Germany

2. Barcelona School of Informatics Polytechnic University of Catalonia Barcelona Spain

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6101

Reference57 articles.

1. LiA SongSL ChenJ LiuX TallentN BarkerK. Tartan: evaluating modern GPU interconnect via a multi‐GPU benchmark suite. Paper presented at: Proceedings of the 2018 IEEE International Symposium on Workload Characterization (IISWC). Raleigh NC;2018:191‐202.

2. LutzC BreßS ZeuchS RablT MarklV. Pump up the volume: processing large data on GPUs with fast interconnects. Paper presented at: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. SIGMOD' 20. Association for Computing Machinery;2020:1633‐1649; New York NY.

3. Amazon.com IncAmazon EC2 instance types;2020.https://aws.amazon.com/ec2/instance‐types/. Accessed August 20 2020.

4. SitaridiE MuellerR KaldeweyT LohmanG RossKA. Massively‐parallel lossless data decompression. Paper presented at: Proceedings of the 2016 45th International Conference on Parallel Processing (ICPP). Philadelphia PA;2016:242‐247; IEEE.

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