XuPy 1.6.0

GPU Accelerated masked arrays with automatic handling of CPU and GPU arrays.

XuPy

XuPy is a comprehensive Python package that provides GPU-accelerated masked arrays and NumPy-compatible functionality using CuPy. It automatically handles GPU/CPU fallback and offers an intuitive interface for scientific computing with masked data.

Features

• GPU Acceleration: Automatic GPU detection with CuPy fallback to NumPy
• Masked Arrays: Full support for masked arrays with GPU acceleration
• Statistical Functions: Comprehensive statistical operations (mean, std, var, min, max, etc.)
• Array Manipulation: Reshape, transpose, squeeze, expand_dims, and more
• Mathematical Functions: Trigonometric, exponential, logarithmic, and rounding functions
• Random Generation: Various random number generators (normal, uniform, etc.)
• Universal Functions: Support for applying any CuPy/NumPy ufunc with mask preservation
• Performance: Optimized for large-scale data processing on GPU

Installation

pip install xupy

Quick Start

import xupy as xp

# Create arrays with automatic GPU detection
a = xp.random.normal(0, 1, (1000, 1000))
b = xp.random.normal(0, 1, (1000, 1000))

# Create masks
mask = xp.random.random((1000, 1000)) > 0.1

# Create masked arrays
am = xp.ma.masked_array(a, mask)
bm = xp.ma.masked_array(b, mask)

# Perform operations (masks are automatically handled)
result = am + bm
mean_val = am.mean()
std_val = am.std()

Performance Benefits

XuPy automatically detects GPU availability and provides significant speedup for large arrays:

• Small arrays (< 1000 elements): CPU (NumPy) may be faster due to GPU overhead
• Medium arrays (1000-10000 elements): GPU provides 2-5x speedup
• Large arrays (> 10000 elements): GPU provides 5-20x speedup depending on operation complexity

GPU Requirements

• CUDA-compatible GPU with compute capability 3.0+
• CuPy package installed (pip install cupy-cuda12x for CUDA 12.x)
• Automatic fallback to NumPy if GPU is unavailable

API Compatibility

XuPy maintains high compatibility with NumPy's masked array interface while leveraging CuPy's optimized operations:

• All standard properties (shape, dtype, size, ndim, T)
• Comprehensive arithmetic operations with mask propagation
• Memory-optimized statistical methods (mean, std, var, min, max) using CuPy's native operations
• Array manipulation methods (reshape, transpose, squeeze)
• Universal function support through apply_ufunc
• Conversion to NumPy masked arrays via asmarray()
• GPU memory management through MemoryContext

GPU Memory Management

XuPy includes an advanced MemoryContext class for efficient GPU memory management:

import xupy as xp

# Basic usage with automatic cleanup
with xp.MemoryContext() as ctx:
    # GPU operations
    data = xp.random.normal(0, 1, (10000, 10000))
    result = data.mean()
# Memory automatically cleaned up on exit

# Advanced features
with xp.MemoryContext(memory_threshold=0.8, auto_cleanup=True) as ctx:
    # Monitor memory usage
    mem_info = ctx.get_memory_info()
    print(f"GPU Memory: {mem_info['used'] / (1024**3):.2f} GB")
    
    # Aggressive cleanup when needed
    if ctx.check_memory_pressure():
        ctx.aggressive_cleanup()
    
    # Emergency cleanup for critical situations
    ctx.emergency_cleanup()

MemoryContext Features

• Automatic Cleanup: Memory freed automatically when exiting context
• Memory Monitoring: Real-time tracking of GPU memory usage
• Pressure Detection: Automatic cleanup when memory usage is high
• Aggressive Cleanup: Force garbage collection and cache clearing
• Emergency Cleanup: Nuclear option for out-of-memory situations
• Object Tracking: Track GPU objects for proper cleanup
• Memory History: Keep history of memory usage over time

Documentation

For detailed documentation, including comprehensive API reference and advanced usage examples, see docs.md.

License

See LICENSE.