Follow these step-by-step instructions to understand and explore Correlating Branch Prediction using the interactive simulator.

Step 1: Understanding the Interface

1. Observe the Initial State

   • Notice that the Global History Register (GHR) starts with all zeros
   • The Pattern History Table (PHT) shows all 2-bit counters initialized to "10" (Weakly Taken)
   • The branch trace area is empty and ready for input
   • Performance statistics show 0% accuracy with no predictions made

2. Familiarize with the Components

   • Assembly Input Panel: Interface to enter branch instructions or load sample programs
   • Global History Register: 4-bit register tracking recent branch outcomes
   • Pattern History Table: 16 entries (2^4) of 2-bit saturating counters
   • Branch Trace Display: Shows executed branches with predictions and actual outcomes
   • Performance Metrics: Real-time accuracy, misprediction rate, and statistics
   • Visualization Controls: Options to highlight patterns and reset simulation

Step 2: Simple Branch Prediction

1. Load a Basic Sample Program

   • Click "Load Sample 1" to get a simple conditional program
   • Observe the assembly code with branch instructions
   • Notice the program structure with if-then-else patterns

2. Generate Initial Branch Trace

   • Click "Generate Trace" to create a sequence of branch outcomes
   • Examine the trace showing branch addresses and their actual outcomes (T/N)
   • Note that this represents the "ground truth" for prediction accuracy

3. Run Basic Prediction

   • Click "Run Simulation" to start the prediction process
   • Watch as each branch is processed:
     • Predict: Use current GHR to index PHT and read counter value
     • Compare: Check prediction against actual outcome
     • Update: Modify counter based on actual result and shift GHR

Step 3: Understanding Pattern Recognition

1. Observe Pattern Learning

   • After several branches, notice how certain GHR patterns become associated with outcomes
   • Watch the PHT counters change: 00→01→10→11 for taken branches, 11→10→01→00 for not-taken
   • Pay attention to prediction accuracy improving over time

2. Analyze History Register Evolution

   • Focus on how the GHR shifts with each branch:
     • Left shift: GHR = (GHR << 1) & 0xF
     • Insert outcome: GHR |= (outcome ? 1 : 0)
   • See how recent branch history provides context for current prediction

3. Study Counter Behavior

   • Notice the hysteresis in 2-bit counters preventing rapid oscillation
   • Observe that strongly taken/not-taken states (11/00) require two consecutive opposite outcomes to change prediction
   • Watch weakly taken/not-taken states (10/01) change prediction after one opposite outcome

Step 4: Exploring Different Branch Patterns

1. Simple Loop Pattern

   • Click "Load Sample 2" for a counting loop example
   • Run the simulation and observe:
     • Loop branches taken repeatedly (creating history pattern like 1111)
     • PHT entry for pattern 1111 quickly learns "strongly taken"
     • Final loop exit creates one misprediction

2. Nested Conditional Pattern

   • Click "Load Sample 3" for nested if-statements
   • Analyze how different execution paths create different history patterns
   • Notice correlation between outer branch outcomes and inner branch behavior

3. Custom Pattern Testing

   • Use the "Custom Assembly" option to write your own branch-heavy code
   • Try patterns like:

     CMP R1, 0
               BNE label1    ; Branch based on condition A
               CMP R2, 0
               BNE label2    ; Branch based on condition B (correlated with A)
               

Step 5: Performance Analysis

1. Accuracy Tracking

   • Monitor the prediction accuracy percentage as simulation progresses
   • Notice the typical learning curve: low initial accuracy improving over time
   • Observe final accuracy levels (typically 85-95% for well-behaved patterns)

2. Misprediction Analysis

   • Click individual mispredicted branches in the trace
   • Study the GHR value and PHT state at the time of misprediction
   • Understand why certain patterns are harder to predict

3. Comparative Performance

   • Use the "Reset & Compare" feature to test different prediction strategies
   • Compare correlating predictor with simpler always-taken/not-taken strategies
   • Analyze improvement in complex vs. simple programs

Step 6: Advanced Pattern Recognition

1. Pattern Interference (Aliasing)

   • Load a program with many different branch patterns
   • Watch for cases where different branches map to the same GHR value
   • Observe how aliasing can cause destructive interference and reduce accuracy

2. Cold Start Behavior

   • Reset the simulation and observe initial predictions
   • Notice how the predictor needs "training time" to learn patterns
   • Study the transition from random accuracy (~50%) to learned behavior

3. Pattern Length Effects

   • Consider how the 4-bit history length limits pattern recognition
   • Think about patterns longer than 4 branches that cannot be distinguished
   • Analyze trade-offs between history length and hardware cost

Step 7: Interactive Experimentation

1. Manual Stepping

   • Use "Step Mode" to advance one branch at a time
   • Manually predict each branch outcome before seeing the result
   • Compare your intuition with the predictor's decision

2. PHT State Examination

   • Click on individual PHT entries to see their prediction history
   • Identify frequently used patterns vs. rarely accessed entries
   • Understand the distribution of pattern usage

3. Custom Scenarios

   • Design specific test cases to explore edge conditions
   • Create programs with known patterns to verify predictor behavior
   • Test worst-case scenarios for prediction accuracy

Step 8: Real-World Applications

1. Loop Behavior Simulation

   • Model typical program loops with the simulator
   • Study how loop nesting affects prediction accuracy
   • Analyze the impact of loop trip counts on performance

2. Conditional Chain Analysis

   • Simulate decision trees and nested conditionals
   • Observe how control flow complexity affects prediction
   • Study the relationship between code structure and predictor effectiveness

3. Performance Impact Assessment

   • Calculate potential pipeline speedup from accurate prediction
   • Estimate cycles saved by avoiding branch misprediction penalties
   • Consider the cost-benefit of different predictor complexities

Step 9: Comparative Study

1. Predictor Comparison Mode

   • Use built-in comparison with simple predictors
   • Measure improvement over static prediction schemes
   • Quantify the benefit of correlation-based prediction

2. Parameter Sensitivity

   • Experiment with different PHT initialization values
   • Consider the impact of different history register lengths
   • Analyze sensitivity to counter width (1-bit vs. 2-bit)

3. Workload Characterization

   • Test the predictor with different types of programs
   • Compare performance on regular vs. irregular branch patterns
   • Study the impact of program size on prediction accuracy

Step 10: Documentation and Analysis

1. Record Experimental Results

   • Document prediction accuracy for different program types
   • Note patterns that are easy vs. difficult to predict
   • Record observations about learning behavior

2. Answer Key Questions

   • How does branch correlation improve prediction accuracy?
   • What are the limitations of correlating predictors?
   • How do hardware constraints affect predictor design?

3. Design Insights

   • Understand trade-offs in predictor complexity
   • Analyze the relationship between accuracy and hardware cost
   • Consider improvements like longer history or hybrid approaches

Expected Learning Outcomes

After completing this procedure, you should understand:

• How correlating branch predictors use history to improve accuracy
• The role of Pattern History Tables in storing prediction patterns
• The impact of 2-bit saturating counters on prediction stability
• Performance benefits of correlation-based prediction over simple schemes
• Hardware trade-offs in predictor design and implementation
• Real-world applications in modern processor architectures

Troubleshooting Tips

• If accuracy seems low, ensure sufficient training time with repeated patterns
• Remember that random branch patterns inherently limit prediction accuracy
• Use the reset function to start fresh experiments
• Check that assembly syntax is correct when using custom programs
• Verify that branch addresses are generating proper trace sequences