Extended Functionality (cupyx)

def scatter_add(a, indices, updates, axis=None):
    """
    Add updates to array at specified indices.
    
    Parameters:
    - a: cupy.ndarray, target array to update
    - indices: cupy.ndarray, indices where updates are applied
    - updates: cupy.ndarray, values to add
    - axis: int or None, axis along which to scatter
    
    Returns:
    cupy.ndarray: Array with scattered additions
    """

def scatter_max(a, indices, updates, axis=None):
    """Apply element-wise maximum at scattered indices."""

def scatter_min(a, indices, updates, axis=None):
    """Apply element-wise minimum at scattered indices."""

def rsqrt(x):
    """
    Reciprocal square root (1/sqrt(x)).
    
    Parameters:
    - x: array-like, input array with positive values
    
    Returns:
    cupy.ndarray: Reciprocal square root of each element
    """

def errstate(**kwargs):
    """
    Context manager for floating-point error handling.
    
    Parameters:
    - all: str, set behavior for all error types
    - divide: str, behavior for division by zero
    - over: str, behavior for overflow
    - under: str, behavior for underflow  
    - invalid: str, behavior for invalid operations
    
    Error behaviors: 'ignore', 'warn', 'raise', 'call', 'print', 'log'
    
    Usage:
    with cupyx.errstate(divide='ignore'):
        result = a / b  # Division by zero won't raise error
    """

def geterr():
    """
    Get current error handling behavior.
    
    Returns:
    dict: Current error handling settings
    """

def seterr(**kwargs):
    """
    Set error handling behavior.
    
    Returns:
    dict: Previous error handling settings
    """

def allow_synchronize(allow=True):
    """
    Context manager to control synchronization behavior.
    
    Parameters:
    - allow: bool, whether to allow synchronization
    
    Usage:
    with cupyx.allow_synchronize(False):
        # Operations run asynchronously
        result = cupy.matmul(a, b)
    """

class DeviceSynchronized:
    """Context manager for device synchronization."""
    def __enter__(self):
        """Enter synchronized context."""
    
    def __exit__(self, *args):
        """Exit synchronized context."""

def empty_pinned(shape, dtype=float, order='C'):
    """
    Create empty array in pinned host memory.
    
    Parameters:
    - shape: int or tuple, array shape
    - dtype: data type, array data type
    - order: {'C', 'F'}, memory layout
    
    Returns:
    numpy.ndarray: Pinned memory array
    """

def empty_like_pinned(a, dtype=None, order='K', subok=True, shape=None):
    """Create empty pinned array with same shape as existing array."""

def zeros_pinned(shape, dtype=float, order='C'):
    """Create zeros array in pinned host memory."""

def zeros_like_pinned(a, dtype=None, order='K', subok=True, shape=None):
    """Create zeros pinned array with same shape as existing array."""

class GeneralizedUFunc:
    """
    Create generalized universal function.
    
    Parameters:
    - definition: str, function signature and operation
    - name: str, function name
    - doc: str, documentation string
    """
    def __init__(self, definition, name=None, doc=None): ...
    
    def __call__(self, *args, **kwargs):
        """Execute generalized ufunc."""

def get_runtime_info(full=False):
    """
    Get CuPy runtime information.
    
    Parameters:
    - full: bool, include detailed information
    
    Returns:
    str: Runtime information including CUDA version, device info, memory usage
    """

def get_array_module(*args):
    """
    Get appropriate array module for SciPy functions.
    
    Returns:
    module: cupyx.scipy if CuPy arrays present, otherwise scipy
    """

def rawkernel(func=None, *, device=False):
    """
    Decorator for JIT compilation of raw CUDA kernels.
    
    Parameters:
    - func: function, kernel function to compile
    - device: bool, whether this is a device function
    
    Usage:
    @cupyx.jit.rawkernel
    def my_kernel(x, y, size):
        tid = jit.threadIdx.x + jit.blockIdx.x * jit.blockDim.x
        if tid < size:
            y[tid] = x[tid] * 2
    """

# CUDA threading model access
threadIdx: object  # Thread index within block
blockDim: object   # Block dimensions  
blockIdx: object   # Block index within grid
gridDim: object    # Grid dimensions
warpsize: int      # Warp size constant

# Built-in functions for JIT kernels
def syncthreads():
    """Synchronize threads in block."""

def syncwarp(mask=0xffffffff):
    """Synchronize threads in warp."""

def range(start, stop=None, step=None):
    """Range function for JIT kernels."""

# Atomic operations
def atomic_add(array, index, value):
    """Atomic addition."""

def atomic_sub(array, index, value):
    """Atomic subtraction."""

def atomic_max(array, index, value):
    """Atomic maximum."""

def atomic_min(array, index, value):
    """Atomic minimum."""

def atomic_cas(array, index, compare, value):
    """Atomic compare-and-swap."""

def profile():
    """
    Context manager for CUDA profiling.
    
    Usage:
    with cupyx.profiler.profile():
        # Code to profile
        result = cupy.matmul(a, b)
    """

def benchmark(func, args=(), kwargs=None, n_warmup=1, n_repeat=1, name=None, n_sync=1):
    """
    Benchmark function performance.
    
    Parameters:
    - func: callable, function to benchmark
    - args: tuple, function arguments
    - kwargs: dict, function keyword arguments  
    - n_warmup: int, number of warmup runs
    - n_repeat: int, number of timing runs
    - name: str, benchmark name
    - n_sync: int, number of synchronizations per run
    
    Returns:
    dict: Timing statistics
    """

def time_range(message=None, color_id=None, *, sync=False):
    """
    Context manager for timing code ranges.
    
    Parameters:
    - message: str, range description
    - color_id: int, color for profiler display
    - sync: bool, synchronize before timing
    """

import cupy as cp
import cupyx

# Scatter operations for sparse updates
indices = cp.array([0, 2, 4, 6, 8])
updates = cp.array([10, 20, 30, 40, 50])
target = cp.zeros(10)

# Add updates at specified indices
result = cupyx.scatter_add(target, indices, updates)
print(result)  # [10,  0, 20,  0, 30,  0, 40,  0, 50,  0]

# Reciprocal square root (common in ML)
x = cp.array([1.0, 4.0, 9.0, 16.0])
rsqrt_result = cupyx.rsqrt(x)  # [1.0, 0.5, 0.333, 0.25]

import cupy as cp
import cupyx

# Handle division by zero gracefully
a = cp.array([1.0, 2.0, 3.0])
b = cp.array([1.0, 0.0, 3.0])

# Without error handling (would raise warning)
# result = a / b

# With error handling
with cupyx.errstate(divide='ignore', invalid='ignore'):
    result = a / b  # [1.0, inf, 1.0] - no warning

# Check current error state
current_settings = cupyx.geterr()
print(current_settings)

import cupy as cp
import cupyx
import numpy as np

# Create pinned memory arrays for faster CPU-GPU transfers
pinned_array = cupyx.zeros_pinned((1000, 1000), dtype=np.float32)

# Fill with data (on CPU)
pinned_array[:] = np.random.rand(1000, 1000).astype(np.float32)

# Fast transfer to GPU
gpu_array = cp.asarray(pinned_array)

# Process on GPU
result = cp.matmul(gpu_array, gpu_array.T)

# Fast transfer back to pinned memory
result_pinned = cupyx.zeros_like_pinned(pinned_array)
result_pinned[:] = cp.asnumpy(result)

import cupy as cp
import cupyx.jit as jit

# JIT-compiled custom kernel
@jit.rawkernel()
def elementwise_multiply(x, y, out, size):
    """Custom element-wise multiplication kernel."""
    tid = jit.threadIdx.x + jit.blockIdx.x * jit.blockDim.x
    if tid < size:
        out[tid] = x[tid] * y[tid]

# Use JIT kernel
a = cp.random.rand(1000000)
b = cp.random.rand(1000000)
result = cp.zeros_like(a)

# Launch kernel
threads_per_block = 256
blocks_per_grid = (len(a) + threads_per_block - 1) // threads_per_block

elementwise_multiply[blocks_per_grid, threads_per_block](a, b, result, len(a))

# More advanced JIT kernel with shared memory
@jit.rawkernel()
def block_sum(data, output, n):
    """Sum elements within each block using shared memory."""
    # Shared memory declaration
    shared = jit.shared_memory.array(256, jit.float32)
    
    tid = jit.threadIdx.x
    bid = jit.blockIdx.x
    idx = bid * jit.blockDim.x + tid
    
    # Load data into shared memory
    if idx < n:
        shared[tid] = data[idx]
    else:
        shared[tid] = 0.0
    
    jit.syncthreads()
    
    # Parallel reduction
    s = jit.blockDim.x // 2
    while s > 0:
        if tid < s:
            shared[tid] += shared[tid + s]
        jit.syncthreads()
        s //= 2
    
    # Write result
    if tid == 0:
        output[bid] = shared[0]

import cupy as cp
import cupyx.profiler as profiler

# Benchmark different implementations
def matmul_standard(a, b):
    return cp.matmul(a, b)

def matmul_dot(a, b):
    return cp.dot(a, b)

# Setup test data
a = cp.random.rand(1000, 1000)
b = cp.random.rand(1000, 1000)

# Benchmark both implementations
stats1 = profiler.benchmark(matmul_standard, (a, b), n_repeat=10)
stats2 = profiler.benchmark(matmul_dot, (a, b), n_repeat=10)

print(f"matmul: {stats1['mean']:.4f} ms")
print(f"dot: {stats2['mean']:.4f} ms")

# Profile code sections
with profiler.profile():
    with profiler.time_range("Matrix multiply"):
        result1 = cp.matmul(a, b)
    
    with profiler.time_range("SVD decomposition"):
        u, s, vh = cp.linalg.svd(result1)
    
    with profiler.time_range("Eigenvalue computation"):
        eigenvals = cp.linalg.eigvals(result1 @ result1.T)

import cupyx

# Get basic runtime info
info = cupyx.get_runtime_info()
print(info)

# Get detailed runtime info
detailed_info = cupyx.get_runtime_info(full=True)
print(detailed_info)

# Example output includes:
# - CUDA version
# - Device properties
# - Memory information
# - Library versions
# - Compilation settings