Latency‐aware adaptive micro‐batching techniques for streamed data compression on graphics processing units-Reference-Cited by-同舟云学术

Latency‐aware adaptive micro‐batching techniques for streamed data compression on graphics processing units

Published:2020-05-04 Issue:11 Volume:33 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Stein Charles M.¹,Rockenbach Dinei A.¹²^ORCID,Griebler Dalvan¹²^ORCID,Torquati Massimo³^ORCID,Mencagli Gabriele³^ORCID,Danelutto Marco³^ORCID,Fernandes Luiz G.²^ORCID

Affiliation:

1. Laboratory of Advanced Research on Cloud Computing Três de Maio Faculty (SETREM) Três de Maio Brazil

2. School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil

3. Computer Science Department University of Pisa Pisa Italy

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul

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.5786

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5. RockenbachDA SteinCM GrieblerD et al. stream processing on multi‐cores with GPUs: parallel programming models' challenges. Paper presented at: Proceedings of the 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2019:834‐841; IEEE; Rio de Janeiro Brazil.

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