Branch Predictor Simulator

Branch Predictor Simulator

Overview

The Branch Predictor Simulator is a Python-based simulation tool to evaluate the performance of differentbranch prediction algorithms. This document will guide you through the steps needed to run the simulator,generate branch traces, and understand how to implement each branch predictor. This simulator helps to gaininsights into branch prediction mechanisms used in modern computer architecture, suitable for educationalpurposes.Running the SimulatorThe process of running the simulator involves the following steps:

Generating the Branch Trace

The branch_trace_generator.py script generates synthetic branch traces that are used by the simulator

to evaluate each branch predictor. To generate a branch trace, you can use the command below:python branch_trace_generator.py --trace <trace_file.csv> --branches<number_of_branches> --seed <random_seed>--trace (optional): path to the branch trace file (default to branch_trace.csv)--branches (optional): Specifies the number of branches to generate. Default is 10,000.

--seed (compulsory): Specifies the random seedThe generated trace is stored in a file called branch_trace.csv and contains two columns:BranchAddress: The address of the branch instruction.

Outcome: The actual outcome (taken or not taken).

Running the Branch Predictor SimulatorOnce the trace file has been generated, you can run the main simulator using the main_simulator.py script.python branch_simulator.py --trace <trace_file.csv> --x<interval_length> --fast--trace (optional): path to the branch trace file (default to branch_trace.csv)--x (optional): Specifies the number of branches for calculating interval-based accuracy (default is 10).--fast (optional): Skips the 2-second pause between intervals, making the simulationrun faster.The simulator reads the branch_trace.csv (unless a different name was provided) file and runs each of theimplemented branch predictors, providing cumulative accuracy statistics during and after the simulation.Logs and Output Data

Real-Time StatisticsThe simulator logs real-time statistics in a file named realtime_stats.txt. This file contains cumulativeaccuracy information for each branch predictor during the simulation, formatted as follows:Predictor, Branches Processed, Cumulative Accuracy (%)Predictor-Specific LogsEach predictor generates a detailed log of predictions during the simulation. These logs are stored in the logsdirectory, with one file per predictor, e.g., logs/One_Bit_log.txt. Each file contains information in theformat:Branch: <branch_number>, Correct: <0_or_1>Branch HistoryTable (BHT) LogsThe simulator also savesthe state of the Branch History Table (BHT) for applicable predictorsin the bht_logsdirectory. Each predictor's BHT log provides insight into the internal state of the predictor after the simulation.Analysis

You can inspect the generated log files to plot the data using external tools like Python, MATLAB, orspreadsheet software for more detailed analysis.Branch PredictorsExplained

1. Static PredictorsStatic Taken / Not Taken: These predictors always predict the branch will be taken (or not taken). Nolearning occurs.

2. One-Bit Branch PredictorMaintains a Branch History Table (BHT) that stores a single bit for each branch address. This bitrepresents whether the branch was previously taken or not. The predictor代写Branch Predictor Simulator  simply repeats the lasoutcome.

3. Two-Bit Branch PredictoUtilizes a two-bit saturating counter for each branch address. The counter ranges from 00 (strongly nottaken) to 11 (strongly taken). Prediction is considered taken if the counter value is 10 or higher. Thecounter should be incremented or decremented based on the actual outcome.

4. Bimodal Branch PredictorUses a fixed-size BHT indexed by the lower bits of the branch address. Each entry in the BHT has a twobit counter similar to the Two-Bit Predictor. The prediction accuracy is improved by reducing aliasing inthe prediction table.

GShare Branch PredictoEmploys global branch history to determine prediction outcomes. It should XOR the global historyregister with the branch address to generate an index into the BHT. This approach helps to correlatepredictions across different branches.

6. Hybrid Branch PredictorCombines the GShare and Bimodal predictors. A choice table determines which predictor (GShare orBimodal) should be trusted for each branch. The choice table is updated to improve the accuracy ofprediction based on which predictor was correct for each branch.Anticipated StepsThese steps can serve as a high-level guideline to aid you during the project:

1. Run branch_trace_generator.py script to generate the trace file given your # as the seedparameter.

2. Implement branch predictors: static, 1-bit, 2-bit, bimodal, gshare, and hybrid branch predictors
3. Run branch_simulator.py script to test out the different branch predictors using the trace filegenerated in step 1.

4. Complete the report.Submission Requirements

1. Project Report
2. Code Implementations of the branch predictors mentioned above.Code ImplementationsYou need to implement various branch predictors in branch_predictors.py file. For each predictor, youneed to implement three functions__init__(self): constructor for the predictor. You can utilize this function to initialize the predictorpredict(self, address): given an address return a prediction (either 0 for not-taken, or 1 for taken)update(self, address, actual_outcome): this function is utilized to update the state of thepredictor with the actual outcome of the branch instruction.Report MinimumRequirementsDescribe in 100 words or less how the provided simulator enable testing various branch predictions.

1. Table with the overall accuracy of each predictor of the generated trace file.
2. Plots that show the branch predictor accuracy over time. The x-axis should be the Number of Branchesand the y-axis should be the Prediction Accuracy (%).

4. Elaborate on the results of the predictors and why some predictors performed better than othersDirectory Structurehe simulator organizes its files and logs as follows:

.

├── branch_predictors.py # Branch predictor implementations

├── branch_trace_generator.py # Generates branch trace files

├── branch_simulator.py # Main branch predictor simulator

├── branch_trace.csv # Generated branch trace file

├── logs/ # Logs for each branch predictor

│ ├── One_Bit_log.txt # Detailed logs for the One-Bit predictor

│ └── ...

├── bht_logs/ # Logs for BHT states

│ ├── GShare_bht.txt # GShare BHT state

│ └── ...

└── realtime_stats.txt # Real-time statistics logSystem Requirements

Python 3.tabulate for tabular progress displayTo install the dependencies, run:pip install tabulate