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

Numerous microarchitectural optimizations unlocked tremendous processingpower for deep neural networks that in turn fueled the AI revolution. With theexhaustion of such optimizations, the growth of modern AI is now gated by theperformance of training systems, especially their data movement. Instead offocusing on single accelerators, we investigate data-movement characteristicsof large-scale training at full system scale. Based on our workload analysis,we design HammingMesh, a novel network topology that provides high bandwidth atlow cost with high job scheduling flexibility. Specifically, HammingMesh cansupport full bandwidth and isolation to deep learning training jobs with twodimensions of parallelism. Furthermore, it also supports high global bandwidthfor generic traffic. Thus, HammingMesh will power future large-scale deeplearning systems with extreme bandwidth requirements.

HammingMesh: A Network Topology for Large-Scale Deep Learning