Concurrent kernel execution and interference analysis on GPUs using deep learning approaches

Author:

Ayub Mohammed,Helmy Tarek

Publisher

Elsevier BV

Subject

General Computer Science

Reference20 articles.

1. An automated framework for characterizing and sub-setting GPGPU loads, in, IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS);Adhinarayanan;IEEE,2016

2. Machine learning-based interference detection in GPFPU concurrent kernel execution, in, 25th International Computer Conference, Computer Society of Iran (CSICC);Alizadeh;IEEE,2020

3. Using machine learning techniques to analyze the performance of concurrent kernel execution on GPUs;Carvalho;Fut. Gen. Comput. Syst.,2020

4. Concurrent kernel execution on graphic processing units;Cassagne;Projet d’Étude et de Recherche, Université de Bordeaux,2013

5. Algorithms for preemptive co-scheduling of kernels on GPUs;Eyraud-Dubois,2020

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems;2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA);2023-08-30

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