A review of CUDA optimization techniques and tools for structured grid computing-Reference-Cited by-同舟云学术

A review of CUDA optimization techniques and tools for structured grid computing

Published:2019-07-26 Issue:4 Volume:102 Page:977-1003
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Al-Mouhamed Mayez A.,Khan Ayaz H.^ORCID,Mohammad Nazeeruddin^ORCID

Funder

King Abdulaziz City for Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Numerical Analysis,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00607-019-00744-1.pdf

Reference48 articles.

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2. Aldinucci M, Danelutto M, Drocco M, Kilpatrick P, Misale C, Peretti Pezzi G, Torquati M (2018) A parallel pattern for iterative stencil + reduce. J Supercomput 74(11):5690–5705. https://doi.org/10.1007/s11227-016-1871-z

3. Almousa A (2017) Experimental evaluation and enhancement of optimizations of annotation-based and automatic parallel code generators for GPUs. PhD thesis, King Fahd University of Petroleum and Minerals

4. Alnæs MS, Logg A, Ølgaard KB, Rognes ME, Wells GN (2014) Unified form language: a domain-specific language for weak formulations of partial differential equations. ACM Trans Math Softw (TOMS) 40(2):9

5. Ansel J, Kamil S, Veeramachaneni K, Ragan-Kelley J, Bosboom J, O’Reilly UM, Amarasinghe S (2014) Opentuner: an extensible framework for program autotuning. In: Proceedings of the 23rd international conference on parallel architectures and compilation. ACM, pp 303–316

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