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Performance study on CUDA GPUs for parallelizing the local ensemble transformed Kalman filter algorithm

  • Published:2011-10-17 Issue:2 Volume:24 Page:167-177
  • ISSN:1532-0626
  • Container-title:Concurrency and Computation: Practice and Experience
  • language:en
  • Short-container-title:Concurrency Computat.: Pract. Exper.

Author:

Blattner Timothy1,Yang Shiming1

Affiliation:

1. Department of Computer Science; University of Maryland, Baltimore County; 1000 Hilltop Circle; Baltimore; MD 21250; USA

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference11 articles.

1. TOP500 Supercomputer Site 2011 http://www.top500.org/lists/2010/11

2. Debunking the 100X GPU vs CPU myth: an evaluation of throughput computing on CPU and GPU;Lee;ACM SIGARCH Computer Architecture News,2010

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1. Adaptive Workload-Balanced Scheduling Strategy for Global Ocean Data Assimilation on Massive GPUs;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11

2. Machine learning based asynchronous computational framework for generalized Kalman filter;Concurrency and Computation: Practice and Experience;2023-01-20

3. Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting;Journal of Computational Physics: X;2020-03

4. Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images;IEEE Access;2020

5. Multisensor Navigation Systems Multicore Architecture;Journal of Aerospace Information Systems;2019-11
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