CUDA Integration

class Device:
    """
    CUDA device context manager.
    
    This class provides a convenient interface for managing CUDA device
    contexts and switching between multiple GPUs.
    """
    def __init__(self, device=None):
        """
        Parameters:
            device: int or Device, optional - CUDA device ID or Device object
        """
    
    def __enter__(self): ...
    def __exit__(self, *args): ...
    
    def use(self):
        """Use this device for subsequent operations."""
    
    @property
    def id(self):
        """Get the device ID."""

def get_device_id():
    """
    Get the current CUDA device ID.
    """

def get_cublas_handle():
    """
    Get the cuBLAS handle for the current device.
    """

def synchronize():
    """
    Synchronize the current device.
    """

def is_available():
    """
    Check if CUDA is available.
    """

def alloc(size):
    """
    Allocate device memory.
    
    Parameters:
        size: int - Size in bytes to allocate
    """

def malloc_managed(size):
    """
    Allocate managed memory (Unified Memory).
    
    Parameters:
        size: int - Size in bytes to allocate
    """

def malloc_async(size):
    """
    Allocate memory asynchronously.
    
    Parameters:
        size: int - Size in bytes to allocate
    """

class BaseMemory:
    """
    Base class for memory objects.
    
    This is the base class for all memory types in CuPy,
    providing common interface for memory management.
    """
    def __init__(self, size): ...
    
    @property
    def ptr(self):
        """Get memory pointer."""
    
    @property
    def size(self):
        """Get memory size in bytes."""

class Memory(BaseMemory):
    """
    Device memory object.
    
    Represents a chunk of device memory allocated on GPU.
    """

class ManagedMemory(BaseMemory):
    """
    Managed memory object.
    
    Represents unified memory accessible from both CPU and GPU.
    """

class MemoryAsync(BaseMemory):
    """
    Asynchronous memory object.
    
    Represents memory allocated asynchronously using memory pools.
    """

class MemoryPointer:
    """
    Pointer to a device memory region.
    
    This class represents a pointer to device memory and provides
    methods for accessing and manipulating memory contents.
    """
    def __init__(self, mem, offset, size, owner=None): ...
    
    def copy_from_device(self, src, size): ...
    def copy_from_device_async(self, src, size, stream=None): ...
    def copy_from_host(self, mem, size): ...
    def copy_from_host_async(self, mem, size, stream=None): ...
    def copy_to_host(self, mem, size): ...
    def copy_to_host_async(self, mem, size, stream=None): ...
    def memset(self, value, size): ...
    def memset_async(self, value, size, stream=None): ...

class UnownedMemory:
    """
    Unowned memory reference.
    
    Represents a reference to memory that is not owned by this object,
    useful for wrapping external memory allocations.
    """

class MemoryPool:
    """
    Memory pool for device memory.
    
    Memory pools reduce allocation overhead by reusing previously
    allocated memory blocks.
    """
    def __init__(self, allocator=None):
        """
        Parameters:
            allocator: function, optional - Custom allocator function
        """
    
    def malloc(self, size): ...
    def free(self, mem): ...
    def free_all_blocks(self): ...
    def free_all_free(self): ...
    def n_free_blocks(self): ...
    def used_bytes(self): ...
    def total_bytes(self): ...

class MemoryAsyncPool:
    """
    Asynchronous memory pool.
    
    Provides asynchronous memory allocation with stream ordering.
    """
    def __init__(self, allocator=None): ...

def set_allocator(allocator):
    """
    Set the memory allocator.
    
    Parameters:
        allocator: function or Allocator - Memory allocator to use
    """

def get_allocator():
    """
    Get the current memory allocator.
    """

class PythonFunctionAllocator:
    """
    Memory allocator using a Python function.
    
    Wraps a Python function to provide custom memory allocation.
    """
    def __init__(self, func, arg): ...

class CFunctionAllocator:
    """
    Memory allocator using a C function.
    
    Wraps a C function pointer for memory allocation.
    """
    def __init__(self, func, arg): ...

def using_allocator(allocator=None):
    """
    Context manager for temporarily using a different allocator.
    
    Parameters:
        allocator: Allocator, optional - Allocator to use temporarily
    """

def alloc_pinned_memory(size):
    """
    Allocate pinned host memory.
    
    Parameters:
        size: int - Size in bytes to allocate
    """

class PinnedMemory:
    """
    Pinned host memory object.
    
    Represents page-locked host memory that can be accessed
    by the GPU for faster transfers.
    """
    def __init__(self, size): ...

class PinnedMemoryPointer:
    """
    Pointer to pinned memory region.
    
    Provides interface for accessing pinned memory contents.
    """
    def __init__(self, mem, offset, size, owner): ...

class PinnedMemoryPool:
    """
    Memory pool for pinned memory.
    
    Manages allocation and reuse of pinned host memory.
    """
    def __init__(self, allocator=None): ...
    def malloc(self, size): ...
    def free(self, mem): ...

def set_pinned_memory_allocator(allocator):
    """
    Set the pinned memory allocator.
    
    Parameters:
        allocator: function - Pinned memory allocator function
    """

class Stream:
    """
    CUDA stream for asynchronous operations.
    
    Streams allow operations to be executed asynchronously and
    can be used to overlap computation and memory transfers.
    """
    def __init__(self, null=False, non_blocking=False, ptds=False):
        """
        Parameters:
            null: bool, optional - Use the default stream if True
            non_blocking: bool, optional - Create a non-blocking stream
            ptds: bool, optional - Use per-thread default stream
        """
    
    def __enter__(self): ...
    def __exit__(self, *args): ...
    
    def synchronize(self): ...
    def add_callback(self, callback, arg): ...
    def record(self, event=None): ...
    def wait_event(self, event): ...
    
    @property
    def ptr(self):
        """Get the stream pointer."""

class ExternalStream:
    """
    Wrapper for external CUDA stream.
    
    Allows integration with CUDA streams created outside of CuPy.
    """
    def __init__(self, ptr): ...

def get_current_stream():
    """
    Get the current CUDA stream.
    """

class Event:
    """
    CUDA event for synchronization.
    
    Events provide a way to monitor the progress of operations
    and synchronize between different streams.
    """
    def __init__(self, block=True, disable_timing=False, interprocess=False):
        """
        Parameters:
            block: bool, optional - Use blocking synchronization
            disable_timing: bool, optional - Disable timing measurements
            interprocess: bool, optional - Enable interprocess usage
        """
    
    def record(self, stream=None): ...
    def synchronize(self): ...
    def query(self): ...
    def elapsed_time(self, end_event): ...

def get_elapsed_time(start_event, end_event):
    """
    Get elapsed time between events.
    
    Parameters:
        start_event: Event - Start event
        end_event: Event - End event
    """

class Graph:
    """
    CUDA graph for capturing and replaying operation sequences.
    
    Graphs allow capturing a sequence of CUDA operations and
    replaying them efficiently with reduced launch overhead.
    """
    def __init__(self): ...
    
    def begin_capture(self, stream=None): ...
    def end_capture(self, stream=None): ...
    def launch(self, stream=None): ...
    def debug_dot_print(self, path): ...

class Function:
    """
    CUDA function object.
    
    Represents a compiled CUDA kernel function that can be launched
    with specified grid and block dimensions.
    """
    def __init__(self, module, name):
        """
        Parameters:
            module: Module - CUDA module containing the function
            name: str - Function name
        """
    
    def __call__(self, grid, block, args, **kwargs): ...
    
    @property
    def attributes(self):
        """Get function attributes."""

class Module:
    """
    CUDA module containing compiled device code.
    
    Modules contain one or more CUDA kernels and can be loaded
    from PTX or CUBIN code.
    """
    def __init__(self): ...
    
    def get_function(self, name): ...
    def get_global(self, name): ...
    def get_texref(self, name): ...
    
    @classmethod
    def load_file(cls, filename): ...
    
    @classmethod
    def load_from_string(cls, source): ...

class MemoryHook:
    """
    Base class for memory allocation hooks.
    
    Memory hooks allow monitoring and customization of memory
    allocation and deallocation operations.
    """
    def alloc_preprocess(self, **kwargs): ...
    def alloc_postprocess(self, mem): ...
    def free_preprocess(self, mem): ...
    def free_postprocess(self, mem): ...

def profile():
    """
    Context manager for CUDA profiling (deprecated).
    
    Note: This is deprecated. Use cupyx.profiler.profile() instead.
    """

def get_local_runtime_version():
    """
    Get the local CUDA runtime version.
    """

def get_cuda_path():
    """
    Get the CUDA installation path.
    """

def get_nvcc_path():
    """
    Get the path to nvcc compiler.
    """

def get_rocm_path():
    """
    Get the ROCm installation path (for AMD GPUs).
    """

def get_hipcc_path():
    """
    Get the path to hipcc compiler (for AMD GPUs).
    """

# CUDA Driver API
driver = cupy.cuda.driver

# CUDA Runtime API  
runtime = cupy.cuda.runtime

# NVRTC Compiler API
nvrtc = cupy.cuda.nvrtc

# Backend library wrappers (lazy-loaded)
cublas = cupy.cuda.cublas      # cuBLAS operations
cusolver = cupy.cuda.cusolver  # cuSOLVER linear algebra
cusparse = cupy.cuda.cusparse  # cuSPARSE sparse operations
curand = cupy.cuda.curand      # cuRAND random numbers
nvtx = cupy.cuda.nvtx          # NVTX profiling markers

import cupy as cp
import cupy.cuda as cuda

# Device management
print(f"Current device: {cuda.get_device_id()}")
print(f"CUDA available: {cuda.is_available()}")

# Using specific devices
with cuda.Device(0):
    # Operations on device 0
    x = cp.array([1, 2, 3])
    
with cuda.Device(1):  # If multiple GPUs available
    # Operations on device 1
    y = cp.array([4, 5, 6])

# Memory management
# Direct memory allocation
mem = cuda.alloc(1024)  # Allocate 1KB
ptr = cuda.MemoryPointer(mem, 0, 1024)

# Using memory pools (recommended)
pool = cuda.MemoryPool()
with cuda.using_allocator(pool.malloc):
    # All allocations use the pool
    large_array = cp.zeros((10000, 10000))

# Memory pool statistics
print(f"Used memory: {pool.used_bytes()} bytes")
print(f"Total memory: {pool.total_bytes()} bytes")

# Stream management for asynchronous operations
stream1 = cuda.Stream()
stream2 = cuda.Stream()

with stream1:
    # Operations executed on stream1
    a = cp.random.rand(1000, 1000)
    b = cp.random.rand(1000, 1000)
    
with stream2:
    # Operations executed on stream2 (can overlap with stream1)
    c = cp.random.rand(1000, 1000)
    d = cp.random.rand(1000, 1000)

# Synchronization
stream1.synchronize()  # Wait for stream1 to complete
stream2.synchronize()  # Wait for stream2 to complete

# Event-based synchronization
event = cuda.Event()
with stream1:
    result1 = cp.dot(a, b)
    event.record()  # Record completion of operations

with stream2:
    stream2.wait_event(event)  # Wait for stream1 operations
    result2 = cp.dot(c, d) + result1  # Uses result from stream1

# Measuring execution time with events
start_event = cuda.Event()
end_event = cuda.Event()

start_event.record()
# Some operations
large_computation = cp.dot(cp.random.rand(5000, 5000), 
                          cp.random.rand(5000, 5000))
end_event.record()
end_event.synchronize()

elapsed_ms = cuda.get_elapsed_time(start_event, end_event)
print(f"Computation took {elapsed_ms} ms")

# Pinned memory for faster transfers
pinned_mem = cuda.alloc_pinned_memory(1000 * 8)  # 1000 float64s
pinned_array = cp.ndarray((1000,), dtype=cp.float64, 
                         memptr=cuda.MemoryPointer(pinned_mem, 0, 1000 * 8))

# Custom kernel example using RawKernel
kernel_code = r'''
extern "C" __global__
void vector_add(float* x, float* y, float* z, int n) {
    int tid = blockDim.x * blockIdx.x + threadIdx.x;
    if (tid < n) {
        z[tid] = x[tid] + y[tid];
    }
}
'''

kernel = cp.RawKernel(kernel_code, 'vector_add')

# Launch custom kernel
n = 1000
x = cp.random.rand(n, dtype=cp.float32)
y = cp.random.rand(n, dtype=cp.float32)
z = cp.zeros(n, dtype=cp.float32)

# Launch with appropriate grid/block size
threads_per_block = 256
blocks_per_grid = (n + threads_per_block - 1) // threads_per_block
kernel((blocks_per_grid,), (threads_per_block,), (x, y, z, n))

# Memory hooks for monitoring
class MemoryTracker(cuda.MemoryHook):
    def __init__(self):
        self.allocated_bytes = 0
        self.freed_bytes = 0
    
    def alloc_postprocess(self, mem):
        self.allocated_bytes += mem.size
        print(f"Allocated {mem.size} bytes")
    
    def free_preprocess(self, mem):
        self.freed_bytes += mem.size
        print(f"Freed {mem.size} bytes")

tracker = MemoryTracker()
# Note: Memory hooks integration depends on CuPy version

# Working with CUDA graphs (for CUDA 10.0+)
if hasattr(cuda, 'Graph'):
    graph = cuda.Graph()
    
    # Capture operations in a graph
    stream = cuda.Stream()
    with stream:
        graph.begin_capture(stream)
        
        # Operations to be captured
        x = cp.random.rand(1000, 1000)
        y = cp.random.rand(1000, 1000)
        z = x @ y
        
        graph.end_capture(stream)
    
    # Replay the graph multiple times efficiently
    for _ in range(10):
        graph.launch(stream)
        stream.synchronize()

# Multi-GPU computation example
def multi_gpu_computation(data_list):
    """Distribute computation across multiple GPUs."""
    n_gpus = cuda.runtime.getDeviceCount()
    streams = []
    results = []
    
    for i, data in enumerate(data_list[:n_gpus]):
        device_id = i % n_gpus
        with cuda.Device(device_id):
            stream = cuda.Stream()
            streams.append(stream)
            
            with stream:
                # Transfer data to this GPU
                gpu_data = cp.asarray(data)
                # Perform computation
                result = cp.sum(gpu_data ** 2)
                results.append(result)
    
    # Synchronize all streams
    for stream in streams:
        stream.synchronize()
    
    return results

# Memory bandwidth benchmark
def memory_bandwidth_test(size_mb=100):
    """Test memory bandwidth between host and device."""
    size_bytes = size_mb * 1024 * 1024
    
    # Host memory
    host_data = cp.asnumpy(cp.random.rand(size_bytes // 8))
    
    # Pinned host memory for faster transfers
    pinned_mem = cuda.alloc_pinned_memory(size_bytes)
    
    # Time regular vs pinned memory transfers
    import time
    
    # Regular host memory
    start = time.time()
    gpu_data1 = cp.asarray(host_data)
    cp.cuda.synchronize()
    regular_time = time.time() - start
    
    # Pinned memory (requires copying to pinned first)
    start = time.time()
    # Copy to pinned then to GPU would be done here
    # This is a simplified example
    pinned_time = time.time() - start
    
    bandwidth_regular = size_mb / regular_time
    print(f"Regular memory bandwidth: {bandwidth_regular:.2f} MB/s")

# Advanced memory pool configuration
def configure_memory_pool():
    """Configure memory pool for optimal performance."""
    # Get the default memory pool
    mempool = cp.get_default_memory_pool()
    
    # Set memory pool growth strategy
    # mempool.set_limit(size=2**30)  # Limit to 1GB
    
    # Monitor memory usage
    print(f"Used bytes: {mempool.used_bytes()}")
    print(f"Total bytes: {mempool.total_bytes()}")
    
    # Force cleanup of unused memory
    mempool.free_all_free()
    
    return mempool

# Context management for robust error handling
def safe_gpu_computation():
    """Example of robust GPU computation with proper cleanup."""
    stream = None
    temp_arrays = []
    
    try:
        stream = cuda.Stream()
        
        with stream:
            # Temporary arrays that need cleanup
            temp1 = cp.random.rand(10000, 10000)
            temp2 = cp.random.rand(10000, 10000)
            temp_arrays.extend([temp1, temp2])
            
            # Main computation
            result = temp1 @ temp2
            
            # Synchronize to ensure completion
            stream.synchronize()
            
        return result
        
    except Exception as e:
        print(f"GPU computation failed: {e}")
        return None
        
    finally:
        # Cleanup resources
        if stream:
            stream.synchronize()
        
        # Force garbage collection of temporary arrays
        del temp_arrays
        cp.get_default_memory_pool().free_all_free()

# Use memory pools to reduce allocation overhead
with cuda.using_allocator(cp.get_default_memory_pool().malloc):
    # All allocations reuse memory from the pool
    data = cp.zeros((10000, 10000))

# Pre-allocate large arrays when possible
workspace = cp.zeros((10000, 10000))  # Reuse this array

# Use appropriate memory types
regular_mem = cuda.alloc(1024)           # Regular device memory
managed_mem = cuda.malloc_managed(1024)   # Unified memory

# Overlap computation and memory transfers
compute_stream = cuda.Stream()
transfer_stream = cuda.Stream()

with transfer_stream:
    # Asynchronous memory transfer
    next_data = cp.asarray(host_data)

with compute_stream:
    # Parallel computation
    result = process_current_data(current_data)

# Choose optimal grid/block dimensions
def optimal_launch_config(n, max_threads_per_block=1024):
    """Calculate optimal CUDA launch configuration."""
    if n <= max_threads_per_block:
        return (1, n)
    else:
        threads_per_block = max_threads_per_block
        blocks_per_grid = (n + threads_per_block - 1) // threads_per_block
        return (blocks_per_grid, threads_per_block)

grid, block = optimal_launch_config(1000000)