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 Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Least Squares with QR-factorization (LSQR) method is a widely used Krylov subspace algorithm to solve sparse rectangu- lar linear systems for tomographic problems. Traditional parallel implementations of LSQR have the potential, depending on the non-zero structure of the matrix, to have significant communication cost. The communication cost can dramatically limit the scalability of the algorithm at large core counts. We describe a scalable parallel LSQR algorithm that utilizes the particular non-zero structure of matrices that occurs in tomographic problems. In particular, we specially treat the kernel component of the matrix, which is relatively dense with a random structure, and the damping component, which is very sparse and highly structured separately. The resulting algorithm has a scalable communication volume with a bounded number of communica- tion neighbors regardless of core count. We present scaling studies from real seismic tomography datasets that illustrate good scalability up to O(10, 000) cores on a Cray XT cluster

A Scalable Parallel LSQR Algorithm for Solving Large-scale Linear System for Tomographic Problems: A Case Study in Seismic Tomography