cudasharp--allows your C # code on the GPU

The CUDASHARP project is designed to allow you to run a C # program on a GPU that supports Cuda.

Sample code:

using system;
using cudasharp;

using managedcuda;
namespace cudasharptest {    static class program     {         static void main ()          {            var ptx =
 CudaSharp.CudaSharp.Translate<int[]> (kernel);
            test (PTX);
            console.readkey (TRUE);         }         static  Void store (int[] arr, int value)         {  &NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;ARR[GPU.THREADX ()  + Gpu.BlockX () &NBSP;*&NBSP;GPU.THREADDIMX ()]&NBSP;=&NBSp;value;         }         //  Resharper disable once inconsistentnaming         static  void kernel (Int[] arr)         {    &NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;&NBSP;VAR&NBSP;TID&NBSP;=&NBSP;GPU.THREADX ()  + 
Gpu.blockx ()  * gpu.threaddimx ();
            var val = arr[tid];             if  (val != 0)                  store (Arr, val
&NBSP;+&NBSP;3);         }         static  Void test (Byte[] ptxfile)         {            const int
 size = 16;             var context = new 
Cudacontext ();             var kernel = context.
Loadkernelptx (ptxfile,  "kernel");             var memory = context.
Allocatememory (4 * size);             var gpumemory = new
 CudaDeviceVariable<int> (memory);             var cpumemory = new
 int[size];             for  (var i = 0;  i < size; i++)                 cpumemory[i] = i - 2;
            gpumemory.copytodevice (cpuMemory);             kernel.
blockdimensions = 4;             kernel.
griddimensions = 4;             kernel.
Run (memory);
            gpumemory.copytohost (cpuMemory);             for  (var i = 0;  i < size; i++)             
    console.writeline ("{0} = {1}",  i, cpumemory[i]);         }     }
 

}

Turn from: http://www.oschina.net/p/cudasharp?utm_source=tuicool&utm_medium=referral