python-optimizer 1.1.0

High-performance Python optimization toolkit with JIT compilation, variable specialization, and runtime optimizations

Python Optimizer 🚀

A high-performance Python optimization toolkit that provides JIT compilation, advanced variable specialization, intelligent caching, and runtime optimizations to accelerate Python code execution without changing language syntax.

🎯 Goal

Accelerate Python program execution by 10-500x through:

• Advanced JIT compilation with Numba and custom optimizations
• GPU acceleration with automatic CPU/GPU dispatching (CUDA/CuPy)
• Intelligent variable specialization with type-aware caching
• Adaptive optimization based on runtime patterns
• Specialization caching with smart memory management
• Zero syntax changes - works with existing Python code

⚡ Performance Results

Real-world performance improvements achieved:

Function Type	Original Time	Optimized Time	Speedup	Cache Hit Rate
Numerical Computation	2.06ms	0.04ms	51x	95%
Financial Metrics	100ms	2ms	50x	88%
Trading Simulation	500ms	5ms	100x	92%
Genetic Algorithm	30s	0.14s	214x	-
Specialized Functions	1.2ms	0.003ms	400x	97%
Array Operations	50ms	0.1ms	500x	91%

Throughput: Up to 36,456 evaluations/second Cache Efficiency: 90%+ hit rates with intelligent eviction

🛠 Installation

# Clone the repository
git clone https://github.com/thinmanj/python-optimizer.git
cd python-optimizer

# Install with pip
pip install -e .

# Or install from PyPI (coming soon)
pip install python-optimizer

🚀 Quick Start

1. Basic JIT Optimization

from python_optimizer import optimize

@optimize(jit=True)
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

# First call compiles, subsequent calls are blazing fast
result = fibonacci(35)  # ~100x faster after compilation

2. Variable Specialization

from python_optimizer import optimize

@optimize(specialize=True, jit=False)
def adaptive_function(data):
    if isinstance(data, list):
        return sum(data)
    elif hasattr(data, '__len__'):
        return len(data)
    return data

# Automatically creates specialized versions for different types
result1 = adaptive_function([1, 2, 3, 4])      # List specialization
result2 = adaptive_function("hello world")      # String specialization  
result3 = adaptive_function(range(100))         # Range specialization
# Each type gets its own optimized version cached for future use

3. Financial Computing Example

import numpy as np
from python_optimizer.jit import calculate_sharpe_ratio_jit

# JIT-compiled financial metrics
returns = np.random.normal(0.001, 0.02, 252)  # Daily returns
sharpe = calculate_sharpe_ratio_jit(returns)   # ~50x faster

4. Trading Strategy Optimization

from python_optimizer.jit import JITBacktestFitnessEvaluator
from python_optimizer.genetic import Individual

# Ultra-fast backtesting with JIT compilation
evaluator = JITBacktestFitnessEvaluator(initial_cash=10000)
individual = Individual(genes={'ma_short': 10, 'ma_long': 30})

# Evaluate strategy performance
metrics = evaluator.evaluate(individual, market_data)
# Achieves 36,000+ evaluations per second

5. GPU Acceleration (NEW! 🎉)

from python_optimizer import optimize, is_gpu_available
import numpy as np

# Check GPU availability
if is_gpu_available():
    print("GPU acceleration available!")

# Enable GPU acceleration with automatic CPU/GPU dispatching
@optimize(gpu=True, gpu_min_size=10_000, jit=False)
def gpu_compute(data):
    return data ** 2 + data * 3

# Automatically uses GPU for large arrays, CPU for small ones
small_array = np.random.randn(1000)       # Uses CPU
large_array = np.random.randn(1_000_000)  # Uses GPU

result1 = gpu_compute(small_array)
result2 = gpu_compute(large_array)  # 5-20x faster on GPU!

6. Advanced Caching & Monitoring

from python_optimizer import (
    get_specialization_stats, 
    clear_specialization_cache,
    configure_specialization,
    get_gpu_info,
    get_gpu_memory_info
)

# Configure specialization behavior
configure_specialization(
    min_calls_for_specialization=3,
    enable_adaptive_learning=True,
    max_cache_size=1000
)

# Monitor performance
stats = get_specialization_stats()
print(f"Cache hit rate: {stats.get('cache_hit_rate', 0):.1%}")
print(f"Specializations created: {stats.get('specializations_created', 0)}")

# Check GPU status
if is_gpu_available():
    gpu_info = get_gpu_info()
    mem_info = get_gpu_memory_info()
    print(f"GPU: {gpu_info['devices'][0]['name']}")
    print(f"Memory: {mem_info.used_gb:.1f}/{mem_info.total_gb:.1f} GB")

📦 Features

Advanced JIT Compilation Engine

• Numba-powered JIT compilation for numerical code
• Automatic type inference and optimization
• GIL-free execution for parallel processing
• Intelligent caching system for compiled functions
• Custom optimization passes for domain-specific code

Intelligent Variable Specialization

• Type-aware specialization with automatic detection
• Adaptive learning from runtime patterns
• Memory-efficient specialized code paths
• Multi-level caching with eviction policies
• Thread-safe specialization cache
• Performance monitoring and analytics

GPU Acceleration (Phase 1 - NEW! 🚀)

• Automatic GPU detection with graceful CPU fallback
• Smart CPU/GPU dispatching based on data size
• GPU memory management with pooling and caching
• CuPy/CUDA integration for array operations
• Configurable thresholds for GPU usage
• Multi-GPU support for device selection
• Zero code changes - same API works on CPU or GPU
• Performance monitoring with GPU statistics

Advanced Caching System

• Specialization cache with multiple eviction policies (LRU, LFU, Adaptive)
• Memory-bounded cache with configurable limits
• Weak references to prevent memory leaks
• TTL-based expiration for temporal optimization
• Thread-safe concurrent access
• Real-time statistics and monitoring

Performance Profiling & Analytics

• Runtime profiling with minimal overhead
• Hot path detection and prioritization
• Performance analytics and reporting
• Specialization effectiveness tracking
• Cache performance monitoring
• Adaptive optimization recommendations

Financial Computing & Trading

• JIT-optimized financial metrics (Sharpe ratio, drawdown, etc.)
• Ultra-fast backtesting engine for trading strategies
• Genetic algorithm optimization for parameter tuning
• High-frequency trading optimizations
• Portfolio optimization with risk management

📖 Documentation

Core Decorator

The @optimize decorator is the main entry point:

from python_optimizer import optimize

@optimize(
    jit=True,                    # Enable JIT compilation
    specialize=True,             # Enable variable specialization
    profile=True,                # Enable performance profiling
    aggressiveness=2,            # Optimization level (0-3)
    cache=True,                  # Enable specialization caching
    gpu=True,                    # Enable GPU acceleration (NEW!)
    gpu_min_size=10_000,         # Minimum size to use GPU (NEW!)
    adaptive_learning=True,      # Enable adaptive optimization
    memory_limit_mb=100,         # Cache memory limit
    min_calls_for_spec=3         # Minimum calls before specialization
)
def your_function(x, y):
    # Your code here - automatically optimized based on usage patterns
    # GPU acceleration for large arrays, JIT for numerical code
    return x * y + compute_heavy_operation()

New Specialization Functions

from python_optimizer import (
    get_specialization_stats,
    clear_specialization_cache,
    configure_specialization,
    get_cache_stats
)

# Configure global specialization behavior
configure_specialization(
    min_calls_for_specialization=3,
    min_performance_gain=0.1,
    enable_adaptive_learning=True,
    max_cache_size=1000,
    max_memory_mb=100
)

# Get performance statistics
stats = get_specialization_stats('function_name')
print(f"Specializations created: {stats.get('specializations_created')}")
print(f"Cache hit rate: {stats.get('cache_hit_rate'):.2%}")
print(f"Performance gain: {stats.get('avg_performance_gain'):.2f}x")

# Global cache statistics
cache_stats = get_cache_stats()
print(f"Total cache entries: {cache_stats['total_entries']}")
print(f"Memory usage: {cache_stats['memory_usage_estimate']:.2f} MB")

# Clear cache when needed
clear_specialization_cache()  # Clear all
clear_specialization_cache('specific_function')  # Clear specific function

JIT Functions

Pre-built JIT-optimized functions:

from python_optimizer.jit import (
    calculate_returns_jit,
    calculate_sharpe_ratio_jit,
    calculate_max_drawdown_jit,
    simulate_strategy_jit
)

Genetic Algorithm Optimization

from python_optimizer.genetic import GeneticOptimizer, ParameterRange

# Define optimization parameters
param_ranges = [
    ParameterRange('learning_rate', 0.001, 0.1, 'float'),
    ParameterRange('hidden_layers', 1, 5, 'int'),
]

# Run optimization
optimizer = GeneticOptimizer(param_ranges, population_size=100)
best_params = optimizer.optimize(fitness_function, generations=50)

🧪 Examples

Check out the examples/ directory for:

• Financial modeling with JIT optimization
• Machine learning parameter optimization
• Numerical computing acceleration
• Trading strategy backtesting

📊 Benchmarks

Run benchmarks to see performance on your system:

python benchmarks/jit_performance_test.py
python benchmarks/genetic_algorithm_benchmark.py
python benchmarks/financial_metrics_benchmark.py

🔧 Configuration

Environment Variables

export PYTHON_OPTIMIZER_JIT_CACHE=1     # Enable JIT cache
export PYTHON_OPTIMIZER_PROFILE=1       # Enable profiling
export PYTHON_OPTIMIZER_PARALLEL=1      # Enable parallel execution

Configuration File

Create python_optimizer.toml:

[jit]
cache_dir = "~/.python_optimizer/cache"
compile_timeout = 30

[profiling]
enabled = true
output_dir = "./profiles"

[specialization]
max_variants = 5
threshold = 100

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

git clone https://github.com/thinmanj/python-optimizer.git
cd python-optimizer

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

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

# Run tests
pytest tests/

# Run linting
black python_optimizer/
isort python_optimizer/
flake8 python_optimizer/

📈 Roadmap

• JIT Compilation Engine - Numba-based optimization
• Advanced Variable Specialization - Type-aware optimization with caching
• Intelligent Caching System - Multi-policy cache with memory management
• Performance Monitoring - Real-time analytics and adaptive learning
• Financial Computing Module - Trading strategy optimization
• Genetic Algorithm - Parameter optimization
• Thread-Safe Operations - Concurrent optimization support
• GPU Acceleration - CUDA/CuPy support with automatic CPU/GPU dispatching ✨
• ML Model Optimization - PyTorch integration with training/inference optimization ✨
• TensorFlow Integration - TensorFlow/Keras model optimization with XLA ✨
• Distributed Computing - Multi-node optimization with Ray/Dask/multiprocessing ✨
• Advanced Profiling - Visual performance analysis tools
• Web Interface - Browser-based optimization dashboard

📄 License

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

🙏 Acknowledgments

• Numba team for excellent JIT compilation framework
• NumPy community for foundational numerical computing
• Trading algorithm researchers for inspiration and validation

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

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