1- A simple tool for porting CUDA to OpenCL:

Swan is a small tool that aids the reversible conversion of existing CUDA codebases to OpenCL. Its main features are the translation of CUDA kernel source-code to OpenCL, and a common API that abstracts both CUDA and OpenCL runtimes. Swan preserves the convenience of the CUDA <<< grid, block >>> kernel launch syntax by generating C source-code for kernel entry-point functions. Possible uses include:

* Evaluating OpenCL performance of an existing CUDA code
* Maintaining a dual-target OpenCL and CUDA code
* Reducing dependence on NVCC when compiling host code
* Support multiple CUDA compute capabilities in a single binary

Swan is developed by the MultiscaleLab, Barcelona, and is available under the GPL2 license.
http://www.multiscalelab.org/swan

2- CLyther = Python + OpenCL:

CLyther is an under-development python tool for OpenCL similar to Cython for C. CLyther is a python language extension intended to make writing OpenCL code as easy as Python itself. CLyther currently only supports a subset of the Python language definition but adds many new features for OpenCL.
CLyther exposes both the OpenCL C library and language to python. It’s features include:
• Fast prototyping of OpenCL code.
• OpenCL kernel function creation using the Python language definition.
• Strong OOP programming in OpenCL code.
• Passing functions as arguments to kernel functions.
• Python emulation mode for OpenCL code.
• Fancy indexing of arrays.
• Dynamic compilation at runtime.
http://clyther.sourceforge.net/

3- PyOpenCL:
PyOpenCL lets you access the OpenCL parallel computation API from Python. Here\’s what sets PyOpenCL apart:
• Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code.
• Completeness. PyOpenCL puts the full power of OpenCL’s API at your disposal, if you wish.
• Convenience. While PyOpenCL\’s primary focus is to make all of OpenCL accessible, it tries hard to make your life less complicated as it does so–without taking any shortcuts.
• Automatic Error Checking. All OpenCL errors are automatically translated into Python exceptions.
• Speed. PyOpenCL’s base layer is written in C++, so all the niceties above are virtually free.
• Helpful, complete documentation and a wiki.
• Liberal licensing (MIT).
http://mathema.tician.de/software/pyopencl

4- LuxRender:
LuxRender is a physically based and unbiased rendering engine. Based on state of the art algorithms, LuxRender simulates the flow of light according to physical equations, thus producing realistic images of photographic quality. LuxRender is free software – both for personal and commercial use – and is licensed under the GPL.
You will find amazing steps and results done to introduce OpenCL support in Luxrender:
http://www.luxrender.net/wiki/index.php?title=Luxrender_and_OpenCL

thanks>