Custom Kernels

class ElementwiseKernel:
    """
    Custom element-wise kernel for parallel array operations.
    
    Parameters:
    - in_params: str, input parameter specification
    - out_params: str, output parameter specification  
    - operation: str, CUDA C code for element-wise operation
    - name: str, kernel name
    - options: tuple, NVCC compiler options
    - preamble: str, code to prepend to kernel
    - loop_prep: str, code before main loop
    - after_loop: str, code after main loop
    """
    def __init__(self, in_params, out_params, operation, name='kernel', 
                 options=(), preamble='', loop_prep='', after_loop=''): ...
    
    def __call__(self, *args, **kwargs):
        """
        Execute kernel on input arrays.
        
        Parameters:
        - *args: input arrays matching in_params specification
        - size: int, number of elements to process
        - stream: cupy.cuda.Stream, CUDA stream for execution
        
        Returns:
        cupy.ndarray or tuple: Output arrays
        """

class RawKernel:
    """
    Raw CUDA kernel from C/C++ source code.
    
    Parameters:
    - code: str, CUDA C/C++ source code
    - name: str, kernel function name in source code
    - options: tuple, NVCC compiler options
    - backend: str, compilation backend ('nvcc' or 'nvrtc')
    - translate_cucomplex: bool, translate cuComplex types
    """
    def __init__(self, code, name, options=(), backend='nvcc', translate_cucomplex=True): ...
    
    def __call__(self, grid, block, args, **kwargs):
        """
        Launch kernel with specified grid and block dimensions.
        
        Parameters:
        - grid: tuple, grid dimensions (blocks)
        - block: tuple, block dimensions (threads per block)
        - args: tuple, kernel arguments
        - shared_mem: int, shared memory size in bytes
        - stream: cupy.cuda.Stream, CUDA stream for execution
        """

class RawModule:
    """
    CUDA module containing multiple functions.
    
    Parameters:
    - code: str, CUDA C/C++ source code
    - options: tuple, NVCC compiler options
    - backend: str, compilation backend
    - translate_cucomplex: bool, translate cuComplex types
    """
    def __init__(self, code, options=(), backend='nvcc', translate_cucomplex=True): ...
    
    def get_function(self, name):
        """
        Get kernel function by name.
        
        Parameters:
        - name: str, function name
        
        Returns:
        RawKernel: Kernel function object
        """

class ReductionKernel:
    """
    Custom reduction kernel for parallel aggregation operations.
    
    Parameters:
    - in_params: str, input parameter specification
    - out_params: str, output parameter specification
    - map_expr: str, mapping expression applied to each element
    - reduce_expr: str, reduction expression for combining values
    - post_map_expr: str, expression applied after mapping
    - identity: str, identity value for reduction
    - name: str, kernel name
    - reduce_type: str, intermediate reduction data type
    - options: tuple, NVCC compiler options
    - preamble: str, code to prepend to kernel
    """
    def __init__(self, in_params, out_params, map_expr, reduce_expr,
                 post_map_expr='', identity='', name='kernel', 
                 reduce_type=None, options=(), preamble=''): ...
    
    def __call__(self, *args, **kwargs):
        """
        Execute reduction kernel.
        
        Parameters:
        - *args: input arrays matching in_params specification
        - axis: int/tuple, axis along which to reduce
        - keepdims: bool, keep reduced dimensions
        - stream: cupy.cuda.Stream, CUDA stream for execution
        
        Returns:
        cupy.ndarray: Reduced result
        """

def fuse(*args, **kwargs):
    """
    Decorator for fusing multiple CuPy operations.
    
    Parameters:
    - kernel_name: str, name for fused kernel
    
    Returns:
    function: Fused function that executes as single kernel
    """

@fuse()
def fused_function(x, y):
    """Example fused function combining multiple operations."""
    return cp.sqrt(x**2 + y**2) * cp.sin(x + y)

import cupy as cp

# Simple element-wise operation
add_kernel = cp.ElementwiseKernel(
    'float32 x, float32 y',  # Input parameters
    'float32 z',             # Output parameters  
    'z = x + y',             # Operation
    'add_kernel'             # Kernel name
)

# Use the kernel
a = cp.random.random((1000, 1000), dtype=cp.float32)
b = cp.random.random((1000, 1000), dtype=cp.float32)
result = add_kernel(a, b)

# More complex element-wise operation
complex_kernel = cp.ElementwiseKernel(
    'float32 x, float32 y, float32 alpha',
    'float32 z',
    '''
    float32 temp = x * x + y * y;
    z = alpha * sqrt(temp) + sin(x + y);
    ''',
    'complex_kernel'
)

result = complex_kernel(a, b, 2.5)

# Matrix addition kernel
matrix_add_code = '''
extern "C" __global__
void matrix_add(float* a, float* b, float* c, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        c[idx] = a[idx] + b[idx];
    }
}
'''

matrix_add_kernel = cp.RawKernel(matrix_add_code, 'matrix_add')

# Launch kernel
n = 1000000
a_gpu = cp.random.random(n, dtype=cp.float32)
b_gpu = cp.random.random(n, dtype=cp.float32)  
c_gpu = cp.zeros(n, dtype=cp.float32)

# Calculate grid and block dimensions
block_size = 256
grid_size = (n + block_size - 1) // block_size

matrix_add_kernel((grid_size,), (block_size,), (a_gpu, b_gpu, c_gpu, n))

# More advanced kernel with shared memory
shared_memory_code = '''
extern "C" __global__
void reduce_sum(float* input, float* output, int n) {
    extern __shared__ float shared_data[];
    
    int tid = threadIdx.x;
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    
    // Load data into shared memory
    shared_data[tid] = (idx < n) ? input[idx] : 0.0f;
    __syncthreads();
    
    // Perform reduction in shared memory
    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
        if (tid < stride) {
            shared_data[tid] += shared_data[tid + stride];
        }
        __syncthreads();
    }
    
    // Write result
    if (tid == 0) {
        output[blockIdx.x] = shared_data[0];
    }
}
'''

reduce_kernel = cp.RawKernel(shared_memory_code, 'reduce_sum')

# Use kernel with shared memory
input_data = cp.random.random(1000000, dtype=cp.float32)
output_size = (len(input_data) + block_size - 1) // block_size
output_data = cp.zeros(output_size, dtype=cp.float32)

shared_mem_size = block_size * 4  # 4 bytes per float
reduce_kernel((output_size,), (block_size,), (input_data, output_data, len(input_data)),
              shared_mem=shared_mem_size)

# Custom sum reduction
sum_kernel = cp.ReductionKernel(
    'T x',           # Input parameter
    'T y',           # Output parameter  
    'x',             # Map expression (identity)
    'a + b',         # Reduction expression
    '0',             # Identity value
    'sum_kernel'     # Kernel name
)

data = cp.random.random((1000, 1000))
total = sum_kernel(data)
row_sums = sum_kernel(data, axis=1)

# Custom standard deviation reduction
std_kernel = cp.ReductionKernel(
    'T x, T mean',   # Input parameters
    'T y',           # Output parameter
    '(x - mean) * (x - mean)',  # Map expression (squared differences)
    'a + b',         # Reduction expression (sum)
    '0',             # Identity value
    'std_kernel'     # Kernel name
)

# Calculate standard deviation
data = cp.random.normal(0, 1, (1000, 1000))
mean_val = cp.mean(data, axis=1, keepdims=True)
variance = std_kernel(data, mean_val, axis=1) / (data.shape[1] - 1)
std_dev = cp.sqrt(variance)

# Module with multiple functions
module_code = '''
extern "C" {

__device__ float square(float x) {
    return x * x;
}

__global__ void vector_norm(float* input, float* output, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        output[idx] = sqrt(square(input[idx]));
    }
}

__global__ void vector_scale(float* input, float* output, float scale, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        output[idx] = input[idx] * scale;
    }
}

}
'''

module = cp.RawModule(code=module_code)
norm_kernel = module.get_function('vector_norm')
scale_kernel = module.get_function('vector_scale')

# Use kernels from module
input_vec = cp.random.random(100000, dtype=cp.float32)
output_vec = cp.zeros_like(input_vec)

# Calculate norms
grid_size = (len(input_vec) + 255) // 256
norm_kernel((grid_size,), (256,), (input_vec, output_vec, len(input_vec)))

# Scale vector
scaled_vec = cp.zeros_like(input_vec)  
scale_kernel((grid_size,), (256,), (input_vec, scaled_vec, 2.5, len(input_vec)))

# Fuse multiple operations for better performance
@cp.fuse(kernel_name='fused_operations')
def complex_computation(x, y, z):
    """Fused function combining multiple mathematical operations."""
    temp1 = cp.sin(x) * cp.cos(y)
    temp2 = cp.exp(-z**2)
    return temp1 * temp2 + cp.sqrt(x**2 + y**2)

# Use fused function
x = cp.random.random((1000, 1000))
y = cp.random.random((1000, 1000))
z = cp.random.random((1000, 1000))

result = complex_computation(x, y, z)  # Executes as single fused kernel

# Compare with unfused version
def unfused_computation(x, y, z):
    """Same computation without fusion."""
    temp1 = cp.sin(x) * cp.cos(y)
    temp2 = cp.exp(-z**2)  
    return temp1 * temp2 + cp.sqrt(x**2 + y**2)

# Fused version is typically faster due to reduced memory traffic

# Kernel with optimized memory access patterns
optimized_code = '''
extern "C" __global__
void optimized_transpose(float* input, float* output, int rows, int cols) {
    __shared__ float tile[32][32+1];  // +1 to avoid bank conflicts
    
    int x = blockIdx.x * blockDim.x + threadIdx.x;
    int y = blockIdx.y * blockDim.y + threadIdx.y;
    
    // Coalesced read from global memory
    if (x < cols && y < rows) {
        tile[threadIdx.y][threadIdx.x] = input[y * cols + x];
    }
    
    __syncthreads();
    
    // Compute transposed coordinates
    x = blockIdx.y * blockDim.y + threadIdx.x;
    y = blockIdx.x * blockDim.x + threadIdx.y;
    
    // Coalesced write to global memory
    if (x < rows && y < cols) {
        output[y * rows + x] = tile[threadIdx.x][threadIdx.y];
    }
}
'''

transpose_kernel = cp.RawKernel(optimized_code, 'optimized_transpose')

# Use optimized kernel
matrix = cp.random.random((4096, 4096), dtype=cp.float32)
transposed = cp.zeros((4096, 4096), dtype=cp.float32)

block_dim = (32, 32)
grid_dim = ((matrix.shape[1] + 31) // 32, (matrix.shape[0] + 31) // 32)

transpose_kernel(grid_dim, block_dim, (matrix, transposed, 
                                     matrix.shape[0], matrix.shape[1]))

# 1. Use appropriate data types
kernel_float32 = cp.ElementwiseKernel(
    'float32 x, float32 y',  # Specify exact precision needed
    'float32 z',
    'z = x + y',
    'add_f32'
)

# 2. Optimize memory access patterns
# Good: Coalesced access
coalesced_kernel = cp.RawKernel('''
extern "C" __global__ void coalesced_access(float* data, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        data[idx] = data[idx] * 2.0f;  // Sequential access
    }
}
''', 'coalesced_access')

# 3. Use shared memory for data reuse
shared_mem_kernel = cp.RawKernel('''
extern "C" __global__ void use_shared_memory(float* input, float* output, int n) {
    extern __shared__ float shared[];
    int tid = threadIdx.x;
    int idx = blockIdx.x * blockDim.x + tid;
    
    // Load to shared memory
    shared[tid] = (idx < n) ? input[idx] : 0.0f;
    __syncthreads();
    
    // Process using shared memory
    if (idx < n) {
        output[idx] = shared[tid] * 2.0f;
    }
}
''', 'use_shared_memory')

# 4. Handle boundary conditions properly
boundary_safe_kernel = cp.ElementwiseKernel(
    'raw T input, int32 size',
    'T output',
    '''
    int idx = i;  // Current thread index
    if (idx < size) {
        output = input[idx] * 2;
    }
    ''',
    'boundary_safe'
)