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

The cost of data movement on parallel systems varies greatly with machinearchitecture, job partition, and nearby jobs. Performance models thataccurately capture the cost of data movement provide a tool for analysis,allowing for communication bottlenecks to be pinpointed. Modern heterogeneousarchitectures yield increased variance in data movement as there are a numberof viable paths for inter-GPU communication. In this paper, we presentperformance models for the various paths of inter-node communication on modernheterogeneous architectures, including the trade-off between GPUDirectcommunication and copying to CPUs. Furthermore, we present a novel optimizationfor inter-node communication based on these models, utilizing all available CPUcores per node. Finally, we show associated performance improvements for MPIcollective operations.

Modeling Data Movement Performance on Heterogeneous Architectures