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/

Zendy Plus

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

Zendy Tools

Zendy Open

Continent-scale datasets challenge hydrological algorithms for processingdigital elevation models. Flow accumulation is an important input for many suchalgorithms; here, I parallelize its calculation. The new algorithm works on oneor many cores, or multiple machines, and can take advantage of large memoriesor cope with small ones. Unlike previous parallel algorithms, the new algorithmguarantees a fixed number of memory access and communication events per rastercell. In testing, the new algorithm ran faster and used fewer resources thanprevious algorithms, exhibiting ~30% strong and weak scaling efficiencies up to48 cores and linear scaling across datasets ranging over three orders ofmagnitude. The largest dataset tested had two trillion (2*10^12) cells. With 48cores, processing required 24 minutes wall-time (14.5 compute-hours). This testis three orders of magnitude larger than any previously performed in theliterature. Complete, well-commented source code and correctness tests areavailable on Github.

Parallel Non-divergent Flow Accumulation For Trillion Cell Digital Elevation Models On Desktops Or Clusters