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+## The dataset used in "Design-Decision Support for GPU Acceleration through Energy Gain and Programming Effort Prediction" manuscript
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+
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+### final
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+The final datasets (csv files) used for building the estimation models in the Manuscript
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+
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+### real-life-dataset
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+Method for extracting datapoints from Rodinia and Polybench benchmark suites.
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+* polybench: The Polybench benchmark suite
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+* rodinia_3.1: The Rodinia benchmark suite
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+* config.txt: A config file that defines the areas that will be measured (corresponding to CPU-hotspots and GPU-kernels)
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+* run_dataset: The scripts to build the datapoints
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+ * run_dataset.py: Usage `python run_dataset.py <app_kernel_name>` The <app_kernel_name> is retrieved from the config.txt
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+ * measure_time_energy.py: Used for measuring execution time and energy consumption on Nvidia Tegra TX1
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+
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+### synthetic-dataset
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+Generates a simple synthetic dataset
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+* generate.py: Usage `python generate.py <num_of_programs> <output_directory>` generates the datapoints (synthetic programs)
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+* run_dataset_script.py: Usage `python run_dataset_script.py <num_of_programs> <input_directory>` runs the datapoints
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+* measure_energy_tegra: Specific scripts for running the synthetic programs and creating energy dataset on Nvidia Tegra TX1.
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+ * remake_dataset_for_tegra.py: Small changes to the granerated programs to run on Tegra TX1
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+ * tegra_measure_new.py: Usage `python tegra_measure_new.py <num_of_programs> <input_directory>` creates the dataset
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