XuPy 1.4.3

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.masked_array(a, mask)
bm = xp.masked_array(b, mask)

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

Comprehensive Examples

Array Creation

import xupy as xp

# Basic array creation
zeros = xp.zeros((3, 3))
ones = xp.ones((3, 3))
eye = xp.eye(3)
identity = xp.identity(3)

# Sequences
linspace = xp.linspace(0, 10, 100)
logspace = xp.logspace(0, 3, 100)
arange = xp.arange(0, 10, 0.5)

# Random arrays
random = xp.random((100, 100))
normal = xp.normal(0, 1, (100, 100))
uniform = xp.uniform(-1, 1, (100, 100))

Masked Array Operations

import xupy as xp
from skimage.draw import disk

# Create data and mask
data = xp.random.normal(0, 1, (500, 500))
mask = xp.ones((500, 500), dtype=bool)

# Create circular mask
circle_coords = disk((250, 250), 200)
mask[circle_coords] = False

# Create masked array
masked_data = xp.masked_array(data, mask)

# Statistical operations
global_mean = masked_data.mean()
global_std = masked_data.std()
global_var = masked_data.var()
global_min = masked_data.min()
global_max = masked_data.max()

# Axis-wise operations
row_means = masked_data.mean(axis=0)
col_sums = masked_data.sum(axis=1)

Mathematical Functions

import xupy as xp

# Create masked array
data = xp.random.normal(0, 1, (100, 100))
mask = xp.random.random((100, 100)) > 0.8
ma = xp.masked_array(data, mask)

# Trigonometric functions
sin_result = ma.sin()
cos_result = ma.cos()
tan_result = ma.tan()

# Inverse trigonometric functions
arcsin_result = ma.arcsin()
arccos_result = ma.arccos()
arctan_result = ma.arctan()

# Hyperbolic functions
sinh_result = ma.sinh()
cosh_result = ma.cosh()
tanh_result = ma.tanh()

# Exponential and logarithmic functions
exp_result = ma.exp()
log_result = ma.log()
log10_result = ma.log10()

# Rounding functions
floor_result = ma.floor()
ceil_result = ma.ceil()
round_result = ma.round(decimals=2)

# Square root
sqrt_result = ma.sqrt()

Array Manipulation

import xupy as xp

# Create masked array
data = xp.random.normal(0, 1, (4, 4))
mask = xp.random.random((4, 4)) > 0.5
ma = xp.masked_array(data, mask)

# Reshape
reshaped = ma.reshape(2, 8)
flattened = ma.flatten()
raveled = ma.ravel()

# Transpose and axes
transposed = ma.T
swapped = ma.swapaxes(0, 1)

# Expand and squeeze dimensions
expanded = ma.expand_dims(axis=1)
squeezed = expanded.squeeze()

# Repeat and tile
repeated = ma.repeat(2, axis=0)
tiled = ma.tile((2, 2))

Advanced Operations

import xupy as xp

# Create complex masked array
data = xp.random.normal(0, 1, (100, 100, 3))
mask = xp.random.random((100, 100, 3)) > 0.9
ma = xp.masked_array(data, mask)

# Multi-axis operations
result = ma.mean(axis=(0, 1))
variance = ma.var(axis=1, ddof=1)

# Boolean operations
any_true = ma.any(axis=0)
all_true = ma.all(axis=1)

# Mask information
masked_count = ma.count_masked()
unmasked_count = ma.count_unmasked()
is_masked = ma.is_masked()

# Compressed data
valid_data = ma.compressed()

# Fill masked values
ma.fill_value(0.0)

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

Key Improvements

• Eliminated redundant functions - Uses CuPy/NumPy directly for basic operations
• Memory-efficient statistical operations - Leverages CuPy's optimized reduction operations
• Proper mask propagation - Maintains mask integrity across all operations
• GPU memory management - Context manager for efficient memory usage

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

Run the memory management demo:

python memory_demo.py

Examples

Run the comprehensive examples script to see XuPy in action:

python examples.py

This script demonstrates:

• GPU detection and information
• Basic masked array operations
• GPU-accelerated computations
• Mathematical functions
• Memory management
• Scientific computing use cases
• Performance comparisons

Documentation

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

License

See LICENSE.

Citation

If you use XuPy in your research, please cite:

@software{xupy2025,
  title={XuPy: GPU-Accelerated Masked Arrays for Scientific Computing},
  author={Ferraiuolo, Pietro},
  year={2024},
  url={https://github.com/pietroferraiuolo/XuPy}
}