Round-Based Mechanism and Job Packing with Model-Similarity-Based Policy for Scheduling DL Training in GPU Cluster
Panissara Thanapol,
Kittichai Lavangnananda,
Franck Leprévost
et al.
Abstract: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. Furthermore, factors such as the number of GPUs for distributed training and batch size significantly impact training efficiency. Addressing the variability in training performance and accounting for these influential… Show more
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