Zeus: interpretable ML-based job scheduling in GPU datacentres

Zeus: interpretable ML-based job scheduling in GPU datacentres

Hardware accelerators such as GPUs are essential for the development of Deep Learning (DL) models - as their training process is compute-intensive. A growing number of organisations have employed expensive multi-tenant GPU clusters to run distributed DL training jobs. Efficient job schedulers are re...

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Bibliographic Details
Main Author: Amrita, Ravishankar
Other Authors: Zhang Tianwei
Format: Final Year Project (FYP)
Language:English
Published: Nanyang Technological University 2022
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Online Access:https://hdl.handle.net/10356/156566

Internet

https://hdl.handle.net/10356/156566

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