Reshaping Geostatistical Modeling and Prediction for Extreme-Scale Environmental Applications-Reference-Cited by-同舟云学术

Reshaping Geostatistical Modeling and Prediction for Extreme-Scale Environmental Applications

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:SC22: International Conference for High Performance Computing, Networking, Storage and Analysis
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Author:

Cao Qinglei¹,Abdulah Sameh²,Alomairy Rabab²,Pei Yu¹,Nag Pratik²,Bosilca George¹,Dongarra Jack¹,Genton Marc G.²,Keyes David E.²,Ltaief Hatem²,Sun Ying²

Affiliation:

1. University of Tennessee,Innovative Computing Laboratory,Knoxville,TN,USA

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10046045/10045783/10046053.pdf?arnumber=10046053

Reference41 articles.

1. Separable approximations of space-time covariance matrices

2. Leveraging PaRSEC Runtime Support to Tackle Challenging 3D Data-Sparse Matrix Problems

3. Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC

4. Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools

5. Spatial Variation

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