MSHGN: Multi-scenario adaptive hierarchical spatial graph convolution network for GPU utilization prediction in heterogeneous GPU clusters-Reference-Cited by-同舟云学术

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MSHGN: Multi-scenario adaptive hierarchical spatial graph convolution network for GPU utilization prediction in heterogeneous GPU clusters

Published:2024-02 Issue: Volume:184 Page:104796
ISSN:0743-7315
Container-title:Journal of Parallel and Distributed Computing
language:en
Short-container-title:Journal of Parallel and Distributed Computing

Author:

Wang Sheng,Chen Shiping,Meng Fei,Shi Yumei

Funder

University of Shanghai for Science and Technology

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference53 articles.

1. Qos-aware dynamic resource allocation for spatial-multitasking gpus;Aguilera,2014

2. Reasoning based workload performance prediction in cloud data centers;Aslam,2019

3. Integrated deep learning method for workload and resource prediction in cloud systems;Bi;Neurocomputing,2021

4. Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning;Chaudhary,2020

5. Exploiting inter-job and intra-job parallelism of distributed machine learning on heterogeneous GPUs;Chen,2022

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