English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Efficient GPU resource scheduling is essential to maximize resourceutilization and save training costs for the increasing amount of deep learningworkloads in shared GPU clusters. Existing GPU schedulers largely rely onstatic policies to leverage the performance characteristics of deep learningjobs. However, they can hardly reach optimal efficiency due to the lack ofelasticity. To address the problem, we propose ONES, an ONline EvolutionaryScheduler for elastic batch size orchestration. ONES automatically manages theelasticity of each job based on the training batch size, so as to maximize GPUutilization and improve scheduling efficiency. It determines the batch size foreach job through an online evolutionary search that can continuously optimizethe scheduling decisions. We evaluate the effectiveness of ONES with 64 GPUs onTACC's Longhorn supercomputers. The results show that ONES can outperform theprior deep learning schedulers with a significantly shorter average jobcompletion time.

Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters