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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As giant dense models advance quality but require large amounts of GPUbudgets for training, the sparsely gated Mixture-of-Experts (MoE), a kind ofconditional computation architecture, is proposed to scale models while keepingtheir computation constant. Specifically, the input tokens are routed by thegate network and only activates part of the expert network. Existing MoEtraining systems only support part of mainstream MoE models (e.g. Top k)training under expensive high-bandwidth GPU clusters. In this paper, we presentHetuMoE, a high-performance large-scale sparse MoE training system built onHetu. HetuMoE provides multiple gating strategies and efficient GPU kernelimplementations. To further improve the training efficiency on commodity GPUclusters (e.g, with only 1 NiC), we introduce the hierarchical AllToAllcommunication that combines hierarchical networks and aggregating messages.Compared with existing state-of-the-art MoE systems, HetuMoE obtains at least15% speedup. Specifically, HetuMoE outperforms DeepSpeed-MoE up to 8.1x underthe switch gate with a batch size of 32. Our code is available at:https://github.com/PKU-DAIR/Hetu.

HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System