CODEWITHSUNDEEP
vb.netgpgpugpu
FraserOfSmeg
FraserOfSmeg

Reputation: 1148

GPU Processing in vb.net

I've got a program that takes about 24 hours to run. It's all written in VB.net and it's about 2000 lines long. It's already multi-threaded and this works perfectly (after some sweat and tears). I typically run the processes with 10 threads but I'd like to increase that to reduce processing time, which is where using the GPU comes into it. I've search google for everything related that I can think of to find some info but no luck.

What I'm hoping for is a basic example of a vb.net project that does some general operations then sends some threads to the GPU for processing. Ideally I don't want to have to pay for it. So something like:

'Do some initial processing eg.
dim x as integer
dim y as integer
dim z as integer
x=int(textbox1.text)
y=int(textbox2.text)
z=x*y
'Do some multi-threaded operations on the gpu eg.
'show some output to the user once this has finished.

Any help or links will be much appreciated. I've read plenty of articles about it in c++ and other languages but I'm rubbish at understanding other languages!

Thanks all!

Fraser

Upvotes: 3

Views: 7990

Answers (1)

Ade Miller
Ade Miller

Reputation: 13713

The VB.NET compiler does not compile onto the GPU, it compiles down to an intermediate language (IL) that is then just-in-time compiled (JITed) for the target architecture at runtime. Currently only x86, x64 and ARM targets are supported. CUDAfy (see below) takes the IL and translates it into CUDA C code. In turn this is compiled with NVCC to generate code that the GPU can execute. Note that this means that you are limited to NVidia GPUs as CUDA is not supported on AMD.

There are other projects that have taken the same approach, for example a Python to CUDA translator in Copperhead.

CUDAfy - A wrapper on top of the CUDA APIs with additional libraries for FFTs etc. There is also a commercial version. This does actually

CUDAfy Translator Using SharpDevelop's decompiler ILSpy as basis the translator converts .NET code to CUDA C.

There are other projects to allow you to use GPUs from .NET languages. For example:

NMath - A set of math libraries that can be used from .NET and are GPU enabled.

There may be others but these seem to be the main ones. If you decide to use CUDAfy then you will still need to invest some time in understanding enough of CUDA and how GPUs work to port your algorithm to fit the GPU data-parallel model. Unless it is something that can be done out of the box with one of the math libraries.

It's important to realize that there is still a performance hit for accessing the GPU from a .NET language. You must pay a cost for moving (marshaling) data from the .NET managed runtime into the native runtime. The overhead here largely depends on not only the size but also the type of data and if it can be marshaled without conversion. This is in addition to the cost of moving data from the CPU to the GPU etc.

Upvotes: 4

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