SchedTune: A Heterogeneity-Aware GPU Scheduler for Deep Learning-Reference-Cited by-同舟云学术

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SchedTune: A Heterogeneity-Aware GPU Scheduler for Deep Learning

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
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Author:

Albahar Hadeel¹,Dongare Shruti¹,Du Yanlin¹,Zhao Nannan²,Paul Arnab K.³,Butt Ali R.¹

Affiliation:

1. Virginia Tech,USA

2. Northwestern Polytechnical University,China

3. BITS Pilani,India

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9825911/9825913/09826092.pdf?arnumber=9826092

Reference55 articles.

2. Scikit-learn: Machine learning in Python;pedregosa;Journal of Machine Learning Research,2011

3. Azure Kubernetes Service (AKS);Microsoft Azure,0

4. Themis: Fair and efficient {GPU} cluster scheduling;mahajan;17th USENIX Symposium on Networked Systems Design and Implementation ( NSDI 20),0

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1. Towards GPU Memory Efficiency for Distributed Training at Scale;Proceedings of the 2023 ACM Symposium on Cloud Computing;2023-10-30

2. PolarisProfiler: A Novel Metadata-Based Profiling Approach for Optimizing Resource Management in the Edge-Cloud Continnum;2023 IEEE International Conference on Service-Oriented System Engineering (SOSE);2023-07

3. HetSev: Exploiting Heterogeneity-Aware Autoscaling and Resource-Efficient Scheduling for Cost-Effective Machine-Learning Model Serving;Electronics;2023-01-03

4. Dynamic GPU Scheduling with Multi-resource Awareness and Live Migration Support;IEEE Transactions on Cloud Computing;2023

5. Scheduling Deep Learning Training in GPU Cluster Using the Model-Similarity-Based Policy;Intelligent Information and Database Systems;2023

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