pycuda-plus 0.2.0

User-friendly library to enhance PyCUDA functionality

PyCUDA Plus

PyCUDA Plus is an enhanced Python library built on top of PyCUDA, designed to simplify GPU programming and execution. It provides high-level abstractions and utilities for working with CUDA kernels, memory management, and context handling, allowing developers to focus on writing efficient CUDA code without dealing with low-level details.

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Key Features

• Kernel Management: Compile, load, and execute custom CUDA kernels easily with the KernelExecutor.
• Memory Management: Simplified allocation and transfer of device and host memory using the MemoryManager.
• Context Handling: Seamless setup and teardown of CUDA contexts with the CudaContextManager.
• Error Checking: Built-in error detection and reporting via CudaErrorChecker.
• Utility Functions: Prebuilt kernels, NumPy support, and grid/block configuration helpers for common operations.
• Grid/Block Configuration: Automate grid and block size calculations for CUDA kernels using GridBlockConfig.
• Performance Profiling: Measure execution time of CUDA kernels with PerformanceProfiler.

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Installation

To install the pycuda_plus library, run:

pip install pycuda_plus

Ensure you have the following prerequisites installed:

• CUDA Toolkit
• PyCUDA
• Compatible NVIDIA GPU drivers

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Getting Started

Example 1: Vector Addition

import numpy as np
from pycuda_plus.core.kernel import KernelExecutor
from pycuda_plus.core.memory import MemoryManager
from pycuda_plus.utils.prebuilt_kernels import get_kernel
from pycuda_plus.core.context import CudaContextManager
from pycuda_plus.core.error import CudaErrorChecker


def vector_addition_example(N):
    kernel = KernelExecutor()
    memory_manager = MemoryManager()  # Using the MemoryManager
    context_manager = CudaContextManager()
    context_manager.initialize_context()

    try:
        A = np.random.rand(N).astype(np.float32)
        B = np.random.rand(N).astype(np.float32)
        C = np.zeros(N, dtype=np.float32)

        vector_add = get_kernel('vector_add')

        # Allocate memory on the GPU
        d_A = memory_manager.allocate_device_array(A.shape, dtype=np.float32)
        d_B = memory_manager.allocate_device_array(B.shape, dtype=np.float32)
        d_C = memory_manager.allocate_device_array(C.shape, dtype=np.float32)

        # Copy data from host to GPU
        memory_manager.copy_to_device(A, d_A)
        memory_manager.copy_to_device(B, d_B)

        block_size = 256
        grid_size = (N + block_size - 1) // block_size

        # Launch the kernel
        kernel.launch_kernel(vector_add, (grid_size, 1, 1), (block_size, 1, 1), d_A, d_B, d_C, np.int32(N))

        error_checker = CudaErrorChecker()
        error_checker.check_errors()

        # Copy the result back to host
        memory_manager.copy_to_host(d_C, C)
        return C
    finally:
        context_manager.finalize_context()


if __name__ == "__main__":
    N = 1000000
    result = vector_addition_example(N)
    print(f"Vector addition result (first 5 elements): {result[:5]}")

Example 2: Matrix Multiplication

import numpy as np
from pycuda_plus.core.kernel import KernelExecutor
from pycuda_plus.core.memory import MemoryManager
from pycuda_plus.core.context import CudaContextManager
from pycuda_plus.core.error import CudaErrorChecker

matrix_multiply_kernel = """
__global__ void matrix_multiply(float *A, float *B, float *C, int N) {
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    if (row < N && col < N) {
        float value = 0;
        for (int k = 0; k < N; ++k) {
            value += A[row * N + k] * B[k * N + col];
        }
        C[row * N + col] = value;
    }
}
"""

def matrix_multiply_example(N):
    kernel = KernelExecutor()
    memory_manager = MemoryManager()
    context_manager = CudaContextManager()
    context_manager.initialize_context()

    try:
        # Host arrays
        A = np.random.rand(N, N).astype(np.float32)
        B = np.random.rand(N, N).astype(np.float32)
        C = np.zeros((N, N), dtype=np.float32)

        # Compile the kernel
        compiled_kernel = kernel.compile_kernel(matrix_multiply_kernel, 'matrix_multiply')

        # Allocate memory on the device
        d_A = memory_manager.allocate_device_array(A.shape, dtype=np.float32)
        d_B = memory_manager.allocate_device_array(B.shape, dtype=np.float32)
        d_C = memory_manager.allocate_device_array(C.shape, dtype=np.float32)

        # Copy data to device
        memory_manager.copy_to_device(A, d_A)
        memory_manager.copy_to_device(B, d_B)

        # Configure grid and block sizes
        block_size = 16
        grid_size = (N + block_size - 1) // block_size

        # Launch the kernel
        kernel.launch_kernel(
            compiled_kernel,
            (grid_size, grid_size, 1),
            (block_size, block_size, 1),
            d_A, d_B, d_C, np.int32(N)
        )

        # Error checking
        error_checker = CudaErrorChecker()
        error_checker.check_errors()

        # Copy the result back to the host
        memory_manager.copy_to_host(d_C, C)
        return C

    finally:
        # Finalize the context
        context_manager.finalize_context()

if __name__ == "__main__":
    N = 512
    result = matrix_multiply_example(N)
    print(f"Matrix multiplication result (first 5x5 elements):\n{result[:5, :5]}")

Example 3: Matrix Addition with Profiling

import numpy as np
from pycuda_plus.core.kernel import KernelExecutor
from pycuda_plus.core.memory import MemoryManager
from pycuda_plus.core.grid_block import GridBlockConfig
from pycuda_plus.core.profiler import PerformanceProfiler
from pycuda_plus.core.context import CudaContextManager

matrix_addition_kernel = """
__global__ void matrix_add(float *A, float *B, float *C, int rows, int cols) {
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;

    if (row < rows && col < cols) {
        int idx = row * cols + col;
        C[idx] = A[idx] + B[idx];
    }
}
"""

def matrix_addition_with_profiling(rows, cols):
    kernel_executor = KernelExecutor()
    memory_manager = MemoryManager()
    grid_config = GridBlockConfig(threads_per_block=256)
    profiler = PerformanceProfiler()
    context_manager = CudaContextManager()

    context_manager.initialize_context()

    try:
        A = np.random.rand(rows, cols).astype(np.float32)
        B = np.random.rand(rows, cols).astype(np.float32)
        C = np.zeros((rows, cols), dtype=np.float32)

        d_A = memory_manager.allocate_device_array(A.shape, dtype=np.float32)
        d_B = memory_manager.allocate_device_array(B.shape, dtype=np.float32)
        d_C = memory_manager.allocate_device_array(C.shape, dtype=np.float32)
        memory_manager.copy_to_device(A, d_A)
        memory_manager.copy_to_device(B, d_B)

        compiled_kernel = kernel_executor.compile_kernel(matrix_addition_kernel, 'matrix_add')

        total_elements = rows * cols
        grid, block = grid_config.auto_config(total_elements)

        grid = (grid[0], grid[0], 1)
        block = (block[0], 1, 1)

        execution_time = profiler.profile_kernel(
            compiled_kernel, grid, block, d_A, d_B, d_C, np.int32(rows), np.int32(cols)
        )
        print(f"Matrix addition kernel execution time: {execution_time:.6f} seconds")

        memory_manager.copy_to_host(d_C, C)

        return C

    finally:
        context_manager.finalize_context()

if __name__ == "__main__":
    rows, cols = 1024, 1024
    result = matrix_addition_with_profiling(rows, cols)
    print(f"Matrix addition result (first 5x5 elements):\n{result[:5, :5]}")

Example 4: Numpy Helper

import numpy as np
from pycuda_plus.core.memory import MemoryManager
from pycuda_plus.utils.numpy_support import NumpyHelper
from pycuda_plus.core.context import CudaContextManager

def example_using_numpy_helper(N):
    # Instantiate required components
    memory_manager = MemoryManager()
    numpy_helper = NumpyHelper()  # We'll keep this in case we need other helper functions
    context_manager = CudaContextManager()

    # Initialize the CUDA context
    context_manager.initialize_context()

    try:
        # Create an array on the host
        host_array1 = np.random.rand(N).astype(np.float32)
        host_array2 = np.random.rand(N).astype(np.float32)

        # Generate a patterned array using NumpyHelper (e.g., a range array)
        d_patterned_array = numpy_helper.generate_patterned_array((N,), 'range')
        patterned_array = numpy_helper.batch_copy_to_host([d_patterned_array])[0]

        # Batch copy arrays to device memory using NumpyHelper
        d_array1, d_array2 = numpy_helper.batch_copy_to_device([host_array1, host_array2])

        # Print some results
        print("Patterned array (first 10 elements):", patterned_array[:10])
        print("Host Array 1 (first 10 elements):", host_array1[:10])
        print("Host Array 2 (first 10 elements):", host_array2[:10])

        # Return the arrays for further use if needed
        return {
            "patterned_array": patterned_array[:10],
            "host_array1": host_array1[:10],
            "host_array2": host_array2[:10],
        }

    finally:
        # Finalize CUDA context
        context_manager.finalize_context()

if __name__ == "__main__":
    N = 10000  # Array size
    results = example_using_numpy_helper(N)

    # Print results
    print("Patterned array (first 10 elements):", results["patterned_array"])
    print("Host Array 1 (first 10 elements):", results["host_array1"])
    print("Host Array 2 (first 10 elements):", results["host_array2"])

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API Documentation

Core Modules

1. KernelExecutor

   • Compile and launch CUDA kernels.
   • Example:

     kernel_executor = KernelExecutor()
     compiled_kernel = kernel_executor.compile_kernel(kernel_code, kernel_name)
     kernel_executor.launch_kernel(compiled_kernel, grid, block, *args)
     

2. MemoryManager

   • Allocate, manage, and transfer memory between host and device.
   • Example:

     memory_manager = MemoryManager()
     device_array = memory_manager.allocate_device_array(shape, dtype)
     memory_manager.copy_to_device(host_array, device_array)
     memory_manager.copy_to_host(device_array, host_array)
     

3. CudaContextManager

   • Simplify CUDA context setup and teardown.
   • Example:

     context_manager = CudaContextManager()
     context_manager.initialize_context()
     context_manager.finalize_context()
     

4. CudaErrorChecker

   • Check for CUDA errors during kernel execution.
   • Example:

     error_checker = CudaErrorChecker()
     error_checker.check_errors()
     

5. GridBlockConfig

   • Automate grid and block size calculation.
   • Example:

     grid_config = GridBlockConfig(threads_per_block=256)
     grid, block = grid_config.auto_config(shape)
     print(f"Grid: {grid}, Block: {block}")
     

6. PerformanceProfiler

   • Measure execution time of CUDA kernels.
   • Example:

     profiler = PerformanceProfiler()
     execution_time = profiler.profile_kernel(kernel, grid, block, *args)
     print(f"Kernel execution time: {execution_time:.6f} seconds")
     

7. NumpyHelper

• Purpose: Provide advanced utilities for integrating NumPy arrays with CUDA device memory using pycuda_plus.
• Functions:
  • reshape_device_array(device_array, new_shape): Reshape a device array into a new shape without changing its contents.
  • generate_patterned_array(shape, pattern): Generate patterned device arrays (e.g., range, linspace) for device-side operations.
  • batch_copy_to_device(numpy_arrays): Batch copy multiple NumPy arrays to device memory.
  • batch_copy_to_host(device_arrays): Batch copy multiple device arrays to host memory.
• Example:

  numpy_helper = NumpyHelper()
  d_patterned_array = numpy_helper.generate_patterned_array((1000,), 'range')
  d_array1, d_array2 = numpy_helper.batch_copy_to_device([array1, array2])
  result = numpy_helper.batch_copy_to_host([d_result])[0]
  

Utility Modules

• numpy_support: Convert between NumPy arrays and GPU memory.
• prebuilt_kernels: Access commonly used CUDA kernels.
• grid_block: Helpers for calculating grid and block dimensions.
• profiler: Tools for profiling CUDA kernel execution.

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Contributing

Contributions are welcome! Please open issues or submit pull requests on the GitHub repository.

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License

PyCUDA Plus is licensed under the MIT License. See the LICENSE file for details.

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Acknowledgments

Built on the foundation of PyCUDA, with additional utilities for enhanced usability and performance.