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Row Performance Analyzer

A comprehensive Python package for analyzing row usage patterns with settling time constraints in memory systems and architectures.

🚀 Features

  • Performance Analysis: Calculate usable row fractions over time
  • Settling Time Modeling: Model row settling behavior after writes
  • Multiple Configurations: Pre-defined system configurations for different scenarios
  • Visualization: Generate performance graphs and charts
  • Command Line Interface: Easy-to-use CLI for quick analysis
  • Extensible: Modular design for custom analysis scenarios

📋 Problem Description

The system has the following characteristics:

  • 1024 total rows that can be written to
  • 1 nanosecond per row write time
  • 1 microsecond settling time after writing a row before it can be used
  • Sequential writing starting from row 0
  • All 1024 rows must be written once writing begins

🏗️ Project Structure

Arch_MSA_Rewrite_Modeling/
├── src/                          # Source code
│   ├── __init__.py              # Package initialization
│   └── row_performance_analyzer.py  # Core analysis functions
├── tests/                       # Unit tests
│   ├── __init__.py
│   └── test_analyzer.py
├── examples/                    # Usage examples
│   └── basic_usage.py
├── docs/                        # Documentation
├── data/                        # Data files
├── main.py                      # Command line interface
├── config.py                    # System configurations
├── setup.py                     # Package setup
├── Makefile                     # Development tasks
├── requirements.txt             # Dependencies
└── README.md                    # This file

🛠️ Installation

Prerequisites

  • Python 3.7 or higher
  • pip

Quick Start

# Clone the repository
git clone https://github.com/shebanow/Arch_MSA_Rewrite_Modeling.git
cd Arch_MSA_Rewrite_Modeling

# Install dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .

Development Installation

# Install with development dependencies
pip install -e ".[dev]"

📖 Usage

Command Line Interface

# Show help
python main.py --help

# Generate both performance graphs
python main.py --generate-graphs

# Analyze usage at specific time
python main.py --analyze-time 1500

# Show settled rows over time
python main.py --settled-rows 2048

# Run complete analysis
python main.py --full-analysis

# Use custom parameters
python main.py --total-rows 2048 --write-time 0.5 --settling-time 2

Python API

from src.row_performance_analyzer import calculate_usable_fraction, generate_graph_1

# Calculate usable fraction at 1024ns
fraction = calculate_usable_fraction(1024)
print(f"Usable fraction: {fraction:.3f}")

# Generate performance graph
generate_graph_1()

Examples

# Run the basic usage example
python examples/basic_usage.py

# Run tests
python -m pytest tests/

# Show configuration summary
python -c "import config; config.print_config_summary()"

📊 Generated Graphs

Graph #1: Usable Fraction vs Usage Time

Shows the fraction of rows that are usable (not settling) as a function of usage time in nanoseconds.

Key Insights:

  • At time 0ns: 100% of rows are usable (1024 rows)
  • At time 1000ns: 100% of rows are still usable (writing hasn't started yet)
  • At time 1024ns: Only 2.3% of rows are usable (24 rows) - this is when writing just finished and most rows are settling
  • At time 2024ns: 100% of rows are usable again (all rows have settled)

Graph #2: Settled Rows vs Time (2048ns Usage)

Shows the number of settled rows over time for a 2048ns usage period, starting with all 1024 rows settled.

Key Insights:

  • Starts with all 1024 rows settled
  • During writing (0-1024ns), settled rows decrease as new rows are written and become unsettled
  • After writing completes, rows gradually settle back
  • Ends with all 1024 rows settled again

⚙️ System Configurations

The package includes several pre-defined system configurations:

  • default: Standard 1024-row system with 1ns write time and 1μs settling time
  • high_perf: High-performance 2048-row system with faster write and settling times
  • low_power: Low-power 512-row system with slower write and settling times
  • long_settling: System with extended 5μs settling time
  • fast_write_slow_settling: System with fast writes but slow settling

🧪 Testing

# Run all tests
make test

# Run tests with coverage
make test-coverage

# Run linting
make lint

# Format code
make format

🛠️ Development

Available Make Targets

make help              # Show all available targets
make install           # Install in development mode
make install-dev       # Install with development dependencies
make test              # Run unit tests
make test-coverage     # Run tests with coverage
make clean             # Clean up generated files
make run-example       # Run the basic usage example
make generate-graphs   # Generate performance graphs
make lint              # Run linting checks
make format            # Format code with black
make quick-analysis    # Run quick analysis
make ci                # Run CI workflow (install, test, lint)
make dev               # Full development workflow

Adding New Features

  1. Add new functions to src/row_performance_analyzer.py
  2. Add tests in tests/test_analyzer.py
  3. Update src/__init__.py to export new functions
  4. Add examples in examples/
  5. Update documentation

📈 Analysis Results

The analysis shows that:

  • The system has a critical period around 1024ns when most rows are settling
  • By 2024ns, all rows have settled and are usable again
  • The net change in settled rows over 2048ns is 0 (starts and ends with all rows settled)
  • Different system configurations show varying performance characteristics

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🏢 Organization

This project is part of the Neurophos organization and focuses on architectural modeling and performance analysis for memory systems.

📞 Support

For questions and support, please open an issue on GitHub or contact the development team.