PyCL-GPU: A GPGPU Framework for Python

PyCL-GPU is a streamlined, Python-based framework for General-Purpose computing on Graphics Processing Units (GPGPU). It abstracts away the boilerplate of PyOpenCL, providing a clean, modular API for numerical and image processing tasks.

Key Features

• Clean API: Manage GPU contexts, buffers, and kernels with minimal code.
• AMP Integrated: Built-in support for the Adaptive Multiprocessing Pool (AMP) to orchestrate parallel GPU tasks across multiple CPU cores.
• Cross-Platform: Works with any OpenCL-compliant hardware (NVIDIA, AMD, Intel).

Project Structure

• framework/: The core library (Context, Buffer, Program, Task management).
• kernels/: OpenCL C kernel source files (e.g., core.cl).
• main_simple.py: A basic vector addition example.
• main.py: An advanced "ensemble" example using multiprocessing (AMP).

Installation

1. Install core dependencies:

   pip install -r requirements.txt

2. Install the amp library (included in this repository's ecosystem):

   cd ../amp_library
   pip install -e .

Quick Start

Simple Task

from framework.task import ParallelTask
task = ParallelTask(kernel_code)
task.execute(global_size=(N,), kernel_args=[a_gpu, b_gpu, c_gpu])

Multiprocessing Ensemble

from framework.task import MultiParallelTask
ensemble = MultiParallelTask(kernel_code)
ensemble.run_async(global_size=(N, N), kernel_args=[A, B, C, N])
ensemble.wait_all()

License

MIT License.