GPU Programming for .NET: Tidepowerd’s GPU.NET gets some improvements, more needed

When I attended the 2010 GPU programming conference hosted by NVIDIA I encounted Tidepowerd, which has a .NET library called GPU.NET for GPU programming.

GPU programming enables amazing performance improvements for certain types of code. Most GPU programming is done in C/C++, but Typepowerd lets you run code in .NET, simply marking any methods you want to run on the GPU with a [kernel] attribute:

[Kernel]

private static void AddGpu(float[] a, float[] b, float[] c)

{

// Get the thread id and total number of threads

int ThreadId = BlockDimension.X * BlockIndex.X + ThreadIndex.X;

int TotalThreads = BlockDimension.X * GridDimension.X;

for (int ElementIndex = ThreadId; ElementIndex < a.Length; ElementIndex += TotalThreads)

{

c[ElementIndex] = a[ElementIndex] + b[ElementIndex];

}

}

GPU.NET is now at version 2.0 and includes Visual Studio Error List and IntelliSense support. This is useful, since some C# code will not run on the GPU. Strings, for example, are not supported. Take a look at this article which lists .NET OpCodes that do not work in GPU.NET.

GPU.NET requires an NVIDIA GPU with CUDA support and a CUDA 3.0 driver. It can run on Mac and Linux using Mono, the open source implementation of .NET. In principle, GPU.NET could also work with AMD GPUs or others via a vendor-specific runtime:

image

but the latest FAQ says:

Support for AMD devices is currently under development, and support for other hardware architectures will follow shortly.

Another limitation is support for multiple GPUs. If you want to do serious supercomputing relatively cheaply, stuffing a PC with a bunch of Tesla GPUs is a great way to do it, but currently GPU.NET only used one GPU per active thread as far as I can tell from this note:

The GPU.NET runtime includes a work-scheduling system which can distribute device method (“kernel”) calls to multiple GPUs in the system; at this time, this only works for applications which call device-based methods from multiple host threads using multiple CPU cores. In a future release, GPU.NET will be able to use multiple GPUs to execute a single method call.

I doubt that GPU.NET or other .NET libraries will ever compete with C/C++ for performance, but ease of use and productivity count for a lot too. Potentially GPU.NET could bring GPU programming to the broad range of .NET developers.

It is also worth checking out hoopoe’s CUDA.NET and OpenCL.NET which are free libraries. I have not done a detailed comparison but would be interested to hear from others who have.

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