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

Matrix Factorization (MF) on large scale data takes substantial time on aCentral Processing Unit (CPU). While Graphical Processing Unit (GPU)s couldexpedite the computation of MF, the available memory on a GPU is finite.Leveraging GPUs require alternative techniques that allow not only parallelismbut also address memory limitations. Synchronization between computation units,isolation of data related to a computational unit, sharing of data betweencomputational units and identification of independent tasks among computationalunits are some of the challenges while leveraging GPUs for MF. We propose ablock based approach to matrix factorization using Stochastic Gradient Descent(SGD) that is aimed at accelerating MF on GPUs. The primary motivation for theapproach is to make it viable to factorize extremely large data sets on limitedhardware without having to compromise on results. The approach addressesfactorization of large scale data by identifying independent blocks, each ofwhich are factorized in parallel using multiple computational units. Theapproach can be extended to one or more GPUs and even to distributed systems.The RMSE results of the block based approach are with in acceptable delta incomparison to the results of CPU based variant and multi-threaded CPU variantof similar SGD kernel implementation. The advantage, of the block basedvariant, in-terms of speed are significant in comparison to other variants.

GPU accelerated matrix factorization of large scale data using block based approach