Parameter based tuning model for optimizing performance on GPU

Author:

Tran Nhat-Phuong,Lee MyunghoORCID,Choi Jaeyoung

Funder

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Reference10 articles.

1. Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proc. IEEE 93(2), 216–231 (2005)

2. Group Khronos: OpenCL. https://www.khronos.org/opencl/ (2015)

3. Nath, R., Tomov, S., Dongarra, J., Agullo, E.: Autotuning dense linear algebra libraries on gpus and overview of the magma library. In: 6th International Workshop on Parallel Matrix Algorithms and Applications (PMAA’10), June (2010)

4. NVIDA: CUDA Toolkit Documentation. http://docs.nvidia.com/cuda/index.html , September (2015)

5. OpenACC-standard.org.: OpenACC. http://www.openacc.org/ , March (2012)

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1. Towards Automatic Block Size Tuning for Image Processing Algorithms on CUDA;Proceedings of the 17th International Conference on Software Technologies;2022

2. Tuning of a Matrix-Matrix Multiplication Algorithm for Several GPUs Connected by Fast Communication Links;Communications in Computer and Information Science;2022

3. IterML: Iterative Machine Learning for Intelligent Parameter Pruning and Tuning in Graphics Processing Units;Journal of Signal Processing Systems;2020-11-06

4. Iterative machine learning (IterML) for effective parameter pruning and tuning in accelerators;Proceedings of the 16th ACM International Conference on Computing Frontiers;2019-04-30

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