Machine Learning Approach for the Predicting Performance of SpMV on GPU-Reference-Cited by-同舟云学术

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Machine Learning Approach for the Predicting Performance of SpMV on GPU

Published:2016-12 Issue: Volume: Page:
ISSN:
Container-title:2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)
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Author:

Benatia Akrem,Ji Weixing,Wang Yizhuo,Shi Feng

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7822825/7823715/07823835.pdf?arnumber=7823835

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Feature-based SpMV Performance Analysis on Contemporary Devices;2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2023-05

2. Invited paper: An Artificial Matrix Generator for Multi-platform SpMV Performance Analysis;2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2023-05

3. Adaptive Hybrid Storage Format for Sparse Matrix–Vector Multiplication on Multi-Core SIMD CPUs;Applied Sciences;2022-09-29

4. Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product;The International Journal of High Performance Computing Applications;2020-08-26

5. Performance modeling of the sparse matrix–vector product via convolutional neural networks;The Journal of Supercomputing;2020-02-04

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