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README.md
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The experiment was run on a desktop workstation running Ubuntu 16.04 Linux with an Intel Core i5 (3.5GHz), 16GB RAM, and a NVidia Geforce GTX 680 4GB GF580 GTX-1.5GB GPU. We use the 'gemm_fast' kernel from the automatic OpenCL kernel tuning library 'CLTune' (https://github.com/CNugteren/CLTune).
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* Note: for this kind of data sets it is usually better to work with the logarithm of the running times (see e.g. Falch and Elster, 'Machine learning-based auto-tuning for enhanced performance portability of OpenCL applications', 2015).
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# Reference
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- Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola.
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- Sobol Tensor Trains for Global Sensitivity Analysis.
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The experiment was run on a desktop workstation running Ubuntu 16.04 Linux with an Intel Core i5 (3.5GHz), 16GB RAM, and a NVidia Geforce GTX 680 4GB GF580 GTX-1.5GB GPU. We use the 'gemm_fast' kernel from the automatic OpenCL kernel tuning library 'CLTune' (https://github.com/CNugteren/CLTune).
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* Note: for this kind of data sets it is usually better to work with the logarithm of the running times (see e.g. Falch and Elster, 'Machine learning-based auto-tuning for enhanced performance portability of OpenCL applications', 2015).
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# Download
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```python
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from datasets import load_dataset
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dataset = load_dataset("Rosykunai/SGEMM_GPU_performance")
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```
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# Reference
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- Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola.
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- Sobol Tensor Trains for Global Sensitivity Analysis.
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