data程序辅导 、讲解 Python编程语言
Branch Predictor Simulator
Overview
The Branch Predictor Simulator is a Python-based simulation tool to evaluate the performance of different
branch 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 gain
insights into branch prediction mechanisms used in modern computer architecture, suitable for educational
purposes.
Running the Simulator
The process of running the simulator involves the following steps:
1. 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 --branches
--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 seed
The 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).
2. Running the Branch Predictor Simulator
Once the trace file has been generated, you can run the main simulator using the main_simulator.py script.
python branch_simulator.py --trace --x --
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 simulation run faster.
The simulator reads the branch_trace.csv (unless a different name was provided) file and runs each of the
implemented branch predictors, providing cumulative accuracy statistics during and after the simulation.
Logs and Output Data
Real-Time Statistics
The simulator logs real-time statistics in a file named realtime_stats.txt. This file contains cumulative
accuracy information for each branch predictor during the simulation, formatted as follows:
Predictor, Branches Processed, Cumulative Accuracy (%)
Predictor-Specific Logs
Each predictor generates a detailed log of predictions during the simulation. These logs are stored in the logs
directory, with one file per predictor, e.g., logs/One_Bit_log.txt. Each file contains information in the
format:
Branch: , Correct: <0_or_1>
Branch History Table (BHT) Logs
The simulator also saves the state of the Branch History Table (BHT) for applicable predictors in the bht_logs
directory. 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, or
spreadsheet software for more detailed analysis.
Branch Predictors Explained
1. Static Predictors
Static Taken / Not Taken: These predictors always predict the branch will be taken (or not taken). No
learning occurs.
2. One-Bit Branch Predictor
Maintains a Branch History Table (BHT) that stores a single bit for each branch address. This bit
represents whether the branch was previously taken or not. The predictor simply repeats the last
outcome.
3. Two-Bit Branch Predictor
Utilizes a two-bit saturating counter for each branch address. The counter ranges from 00 (strongly not
taken) to 11 (strongly taken). Prediction is considered taken if the counter value is 10 or higher. The
counter should be incremented or decremented based on the actual outcome.
4. Bimodal Branch Predictor
Uses a fixed-size BHT indexed by the lower bits of the branch address. Each entry in the BHT has a two bit counter similar to the Two-Bit Predictor. The prediction accuracy is improved by reducing aliasing in
the prediction table.
5. GShare Branch Predictor
Employs global branch history to determine prediction outcomes. It should XOR the global history
register with the branch address to generate an index into the BHT. This approach helps to correlate
predictions across different branches.
6. Hybrid Branch Predictor
Combines the GShare and Bimodal predictors. A choice table determines which predictor (GShare or
Bimodal) should be trusted for each branch. The choice table is updated to improve the accuracy of
prediction based on which predictor was correct for each branch.
Anticipated Steps
These 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 seed
parameter.
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 file
generated in step 1.
4. Complete the report.
Submission Requirements
1. Project Report
2. Code Implementations of the branch predictors mentioned above.
Code Implementations
You need to implement various branch predictors in branch_predictors.py file. For each predictor, you
need to implement three functions
__init__(self): constructor for the predictor. You can utilize this function to initialize the predictor
predict(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 the
predictor with the actual outcome of the branch instruction.
Report Minimum Requirements
1. Describe in 100 words or less how the provided simulator enable testing various branch predictions.
2. Table with the overall accuracy of each predictor of the generated trace file.
3. Plots that show the branch predictor accuracy over time. The x-axis should be the Number of Branches
and the y-axis should be the Prediction Accuracy (%).
4. Elaborate on the results of the predictors and why some predictors performed better than others.
Directory Structure
The 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 log
System Requirements
Python 3.x
tabulate for tabular progress display
To install the dependencies, run:
pip install tabulate