A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization-Reference-Cited by-同舟云学术

A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
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Author:

Cao Qinglei¹,Alomairy Rabab²,Pei Yu¹,Bosilca George¹,Ltaief Hatem²,Keyes David²,Dongarra Jack¹

Affiliation:

1. University of Tennessee,Innovative Computing Laboratory,US

2. Extreme Computing Research Center, King Abdullah University of Science and Technology,Division of Computer, Electrical, and Mathematical Sciences and Engineering,KSA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9820609/9820610/09820680.pdf?arnumber=9820680

Reference40 articles.

1. Taking Advantage of Hybrid Systems for Sparse Direct Solvers via Task-Based Runtimes

2. Multilevel interpolation of scattered data using ${\mathcal{H}}$-matrices

3. Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance

4. Error Estimates for Interpolation by Compactly Supported Radial Basis Functions of Minimal Degree

5. Radial Basis Functions

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