In the realm of High-Performance Computing (HPC), where every cycle counts, the Branch Predictor Analysis Suite emerges as a cutting-edge toolset, crucial for optimizing processor efficiency. This advanced project is ingeniously crafted to delve into the intricacies of branch prediction strategies, a cornerstone in modern CPU design. It encompasses a suite of sophisticated scripts, meticulously designed for generating comprehensive branch prediction datasets and for conducting an in-depth comparative analysis of various state-of-the-art predictors. This includes, but is not limited to, Always Taken, Never Taken, Bimodal, GShare, and Perceptron predictors. The suite stands as an invaluable asset for HPC enthusiasts, researchers, and professionals, offering insights that drive the next generation of processor performance enhancements.
datagen.py: Generates synthetic datasets representing branch prediction scenarios. compare.py: Compares the performance of various branch predictors using a generated dataset. realistic_branch_predictor_dataset.csv: A sample dataset generated by datagen.py. Getting Started Prerequisites Python 3.x NumPy (for datagen.py) TensorFlow (optional, only if using LSTM-based predictor) Setup Clone the repository or download the project files to a local directory.
Run datagen.py: This script generates a synthetic dataset of branch addresses and their outcomes (taken or not taken). The generated dataset is saved as realistic_branch_predictor_dataset.csv. Running the Comparison Run compare.py: This script loads the generated dataset and runs various branch prediction algorithms on it. The accuracy of each predictor is calculated and printed to the console. Script Details datagen.py Functionality: Generates a dataset of synthetic branch addresses and outcomes. Output: CSV file with branch addresses and their outcomes. compare.py Functionality: Compares the accuracy of different branch predictors. Predictors: Always Taken: Predicts that every branch will be taken. Never Taken: Predicts that no branch will be taken. Bimodal: A simple predictor that adjusts its prediction based on previous outcomes. GShare: A more complex predictor using a history register and a pattern table. Perceptron: A basic machine learning approach using a perceptron model.
Run the scripts in the following order:
datagen.py to generate the dataset. compare.py to perform the comparison using the generated dataset. Customization Dataset Generation: Modify datagen.py to change the patterns of branch predictions in the dataset. Adding Predictors: New predictors can be added to compare.py for comparison.