Evaluating execution time predictions on GPU kernels using an analytical model and machine learning techniques
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Published:2023-01
Issue:
Volume:171
Page:66-78
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ISSN:0743-7315
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Container-title:Journal of Parallel and Distributed Computing
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language:en
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Short-container-title:Journal of Parallel and Distributed Computing
Author:
Amaris Marcos,Camargo Raphael,Cordeiro Daniel,Goldman Alfredo,Trystram Denis
Funder
Nvidia
Fundação de Amparo à Pesquisa do Estado de São Paulo
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Subject
Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software
Reference41 articles.
1. Predicting execution time of cuda kernel using static analysis;Alavani,2018
2. A simple BSP-based model to predict execution time in GPU applications;Amaris,2015
3. Hybrid, scalable, trace-driven performance modeling of gpgpus;Arafa,2021
4. An adaptive performance modeling tool for GPU architectures;Baghsorkhi;SIGPLAN Not.,2010
Cited by
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