Using machine learning techniques to analyze the performance of concurrent kernel execution on GPUs-Reference-Cited by-同舟云学术

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Using machine learning techniques to analyze the performance of concurrent kernel execution on GPUs

Published:2020-12 Issue: Volume:113 Page:528-540
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Carvalho Pablo,Clua Esteban,Paes Aline,Bentes Cristiana,Lopes Bruno,Drummond Lúcia Maria de A.

Funder

CoordenaÃ§Ã£o de AperfeiÃ§oamento de Pessoal de NÃvel Superior

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃ³gico

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference48 articles.

1. Kernel concurrency opportunities based on GPU benchmarks characterization;Carvalho;Cluster Comput.,2020

2. Machine Learning: An Artificial Intelligence Approach;Michalski,2013

3. An introduction to variable and feature selection;Guyon;J. Mach. Learn. Res.,2003

4. The feature importance ranking measure;Zien,2009

5. Xgboost: A scalable tree boosting system;Chen,2016

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1. Ensemble selector mixed with pareto optimality to feature reduction;Applied Soft Computing;2023-11

2. Concurrent kernel execution and interference analysis on GPUs using deep learning approaches;Journal of King Saud University - Computer and Information Sciences;2022-11

3. A Survey of GPU Multitasking Methods Supported by Hardware Architecture;IEEE Transactions on Parallel and Distributed Systems;2022-06-01

4. An Accelerated and Flexible SIFT Parallel-Computing Approach Based on the General Multi-Core Platform;International Journal of Pattern Recognition and Artificial Intelligence;2022-03-28

5. Embedded Intelligence: State-of-the-Art and Research Challenges;IEEE Access;2022

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