Round-Based Mechanism and Job Packing with Model-Similarity-Based Policy for Scheduling DL Training in GPU Cluster
Graphics Processing Units (GPUs) are employed for their parallel processing capabilities, which are essential to train deep learning (DL) models with large datasets within a reasonable time. However, the diverse GPU architectures exhibit variability in training performance depending on DL models. Fu...
Main Authors: | Panissara Thanapol, Kittichai Lavangnananda, Franck Leprévost, Arnaud Glad, Julien Schleich, Pascal Bouvry |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-03-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/14/6/2349 |
Similar Items
-
Cost Efficient GPU Cluster Management for Training and Inference of Deep Learning
by: Dong-Ki Kang, et al.
Published: (2022-01-01) -
Fast CNN Stereo Depth Estimation through Embedded GPU Devices
by: Cristhian A. Aguilera, et al.
Published: (2020-06-01) -
Towards efficiently solving the rubik’s cube with deep reinforcement learning and recursion
by: Roshan M. Mahindra, et al.
Published: (2024-01-01) -
A GPU Scheduling Framework to Accelerate Hyper-Parameter Optimization in Deep Learning Clusters
by: Jaewon Son, et al.
Published: (2021-02-01) -
A Field Programmable Gate Array Placement Methodology for Netlist-Level Circuits with GPU Acceleration
by: Meng Liu, et al.
Published: (2023-12-01)