Best approach for GPGPU/CUDA/OpenCL in Java?

AFAIK, JavaCL / OpenCL4Java is the only OpenCL binding that is available on all platforms right now (including MacOS X, FreeBSD, Linux, Windows, Solaris, all in Intel 32, 64 bits and ppc variants, thanks to its use of JNA).

It has demos that actually run fine from Java Web Start at least on Mac and Windows (to avoid random crashes on Linux, please see this wiki page, such as this Particles Demo.

It also comes with a few utilities (GPGPU random number generation, basic parallel reduction, linear algebra) and a Scala DSL.

Finally, it's the oldest bindings available (since june 2009) and it has an active user community.

(Disclaimer: I'm JavaCL's author :-))

Well CUDA is a modification of C, to write CUDA kernel you have to code in C, and then compile to executable form with nvidia's CUDA compiler. Produced native code could then be linked with Java using JNI. So technically you can't write kernel code from Java. There is JCUDA http://www.jcuda.de/jcuda/JCuda.html, it provides you with cuda's apis for general memory/device menagement and some Java methods that are implemented in CUDA and JNI wrapped (FFT, some linear algebra methods.. etc etc..).

On the other hand OpenCL is just an API. OpenCL kernels are plain strings passed to the API so using OpenCL from Java you should be able to specify your own kernels. OpenCL binding for java can be found here http://www.jocl.org/.

You may also consider Aparapi. It allows you to write your code in Java and will attempt to convert bytecode to OpenCL at runtime.

Full disclosure. I am the Aparapi developer.

I've been using JOCL and I'm very happy with it.

The main disadvantage of OpenCL over CUDA (at least for me) is the lack of available libraries (Thrust, CUDPP, etc). However CUDA can be easily ported to OpenCL, and by looking at how those libraries work (algorithms, strategies, etc) is actually very nice as you learn a lot with it.