Memory Profiler Tool for Embedded Systems

A lightweight memory profiling toolkit specifically designed for embedded systems engineers running Python on resource-constrained devices. This tool provides byte-level precision tracking, static analysis, fragmentation visualization, and cross-platform memory prediction.

Features

1. Micro-allocation Tracking

• Track every memory allocation down to individual bytes
• Microsecond-precision timestamps
• Full stack traces for allocation sources
• Size class categorization (tiny, small, medium, large)
• Real-time allocation event streaming

2. Static Memory Analysis

• Pre-deployment memory usage estimation
• Code path analysis
• Memory hotspot detection
• Device constraint validation
• Bytecode analysis for memory prediction

3. Memory Fragmentation Analysis

• Text-based memory visualization
• Fragmentation percentage calculation
• Free block distribution tracking
• Defragmentation strategy suggestions
• Memory layout visualization

4. Memory Optimization Recommendations

• Data structure optimization suggestions
• Object pooling candidates identification
• Algorithm efficiency recommendations
• Redundancy detection
• Implementation effort estimation

5. Cross-Platform Memory Prediction

• Support for ARM32/64, x86/64, ESP32, STM32, AVR
• Platform-specific memory modeling
• Alignment requirement calculations
• Memory overhead predictions
• Cross-platform comparison reports

Installation

pip install -r requirements.txt

Usage Examples

Micro-allocation Tracking

from src.micro_tracker import MicroTracker

# Track allocations in your code
with MicroTracker() as tracker:
    # Your code here
    data = [i for i in range(1000)]
    cache = {'key': 'value'}
    
    # Get allocation statistics
    stats = tracker.get_stats()
    print(f"Total allocations: {stats.total_count}")
    print(f"Current memory: {stats.current_memory} bytes")
    print(f"Peak memory: {stats.peak_memory} bytes")
    
    # Stream real-time allocations
    tracker.stream_allocations()

Static Analysis

from src.static_analyzer import StaticAnalyzer
from pathlib import Path

# Analyze before deployment
analyzer = StaticAnalyzer(device_memory_limit=256*1024)  # 256KB limit
report = analyzer.analyze_file(Path("your_code.py"))

print(f"Estimated memory: {report.total_estimate.average_bytes} bytes")
print(f"Memory hotspots: {report.memory_hotspots}")
print(f"Recommendations: {report.recommendations}")

Fragmentation Analysis

from src.fragmentation import FragmentationAnalyzer

# Analyze memory fragmentation
analyzer = FragmentationAnalyzer(memory_size=64*1024)  # 64KB

# Simulate allocations
allocations = [
    (1024, "buffer"),
    (512, "cache"),
    (2048, "data"),
]

report = analyzer.analyze_allocation_pattern(allocations)
print(report.visualization)
print(f"Fragmentation: {report.metrics.fragmentation_percentage}%")

Memory Optimization

from src.optimizer import MemoryOptimizer

# Get optimization suggestions
optimizer = MemoryOptimizer()
report = optimizer.analyze_file(Path("your_code.py"))

for suggestion in report.suggestions:
    print(f"Priority {suggestion.priority}: {suggestion.suggested_approach}")
    print(f"Estimated savings: {suggestion.estimated_savings} bytes")
    print(f"Effort: {suggestion.implementation_effort}")

Cross-Platform Prediction

from src.cross_platform import CrossPlatformPredictor, Architecture

# Predict memory usage across platforms
predictor = CrossPlatformPredictor()

allocations = [
    ("sensor_data", 4096),
    ("message_buffer", 1024),
    ("state_machine", 512),
]

report = predictor.predict_memory(
    allocations, 
    [Architecture.ESP32, Architecture.STM32, Architecture.AVR]
)

for arch, prediction in report.predictions.items():
    print(f"{arch.value}: {prediction.peak_memory} bytes")
    if prediction.warnings:
        print(f"  Warnings: {prediction.warnings}")

Running Tests

To run all tests and generate the JSON report:

pytest --json-report --json-report-file=pytest_results.json

To run specific test modules:

pytest tests/test_micro_tracker.py -v
pytest tests/test_static_analyzer.py -v
pytest tests/test_fragmentation.py -v
pytest tests/test_optimizer.py -v
pytest tests/test_cross_platform.py -v

Project Structure

memory_profiler_tool_embedded_engineer/
├── src/
│   ├── __init__.py
│   ├── micro_tracker.py      # Byte-level allocation tracking
│   ├── static_analyzer.py    # Pre-deployment analysis
│   ├── fragmentation.py      # Fragmentation metrics
│   ├── optimizer.py          # Optimization recommendations
│   └── cross_platform.py     # Cross-compilation prediction
├── tests/
│   ├── __init__.py
│   ├── test_micro_tracker.py
│   ├── test_static_analyzer.py
│   ├── test_fragmentation.py
│   ├── test_optimizer.py
│   └── test_cross_platform.py
├── requirements.txt
└── README.md

Design Considerations

This tool is designed with embedded systems constraints in mind:

• Minimal overhead: The profiler itself uses minimal memory
• No external dependencies: Pure Python implementation
• Text-based output: No GUI components, suitable for embedded environments
• Byte-level precision: Critical for memory-constrained devices
• Platform awareness: Understands different embedded architectures

Best Practices

1. Pre-deployment Analysis: Always run static analysis before deploying to embedded devices
2. Regular Profiling: Profile during development to catch memory issues early
3. Platform Testing: Test memory usage predictions against actual device measurements
4. Fragmentation Monitoring: Check fragmentation patterns in long-running applications
5. Optimization Priority: Focus on high-priority, low-effort optimizations first

Limitations

• Dynamic memory tracking requires the Python garbage collector
• Static analysis provides estimates, not guarantees
• Platform predictions are based on heuristics and may vary from actual usage
• Some Python internals may not be fully tracked

Contributing

When contributing to this project:

1. Ensure all tests pass
2. Add tests for new features
3. Keep embedded constraints in mind
4. Document memory overhead of new features
5. Test on multiple platforms when possible