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Stargazer is a regression-based GPU design space exploration tool. It can be used to efficiently survey extremely large hardware and software design spaces of modern GPUs. For more information, please visit [http://www.princeton.edu/~wjia/stargazer]. Stargazer 1.0, Wenhao Jia [email@example.com], Princeton University, 2012 This is a list of important files included in the package. Scripts have embedded comments which further explain their usage. 1. stargazer.R: The core stepwise regression method. 2. matrix.csv: Example input file from simulating a matrix multiply program. 3. matrix-training.csv: 60% of the matrix.csv file, randomly selected. 4. matrix-test.csv: 40% of the matrix.csv file, randomly selected. 5. gpgpusim/README: Directions for how to use Python scripts in this folder. 6. gpgpusim/simulate.py: Run GPGPU-Sim multiple times with varying parameters. 7. gpgpusim/collect.py: Collect simulation results and generate CSV files. 8. gpgpusim/split.py: Split a CSV file into a traing set and a test set. 9. gpgpusim/filter.py: Scan a CSV file s.t. only lines w/ certain values remain. 10. R/README: Directions for how to use R scripts in this folder. 11. R/accuracy.R: Measure model prediction accuracy w/ training and test sets. 12. R/size.R: Show how to obtain the prediction accuracy vs. sample size curve. 13. R/time.R: Demonstrates how to time the regression process.