public static float[] RunMatrixMul(float[][] a, float[][] b, int N)
{
//Create context and accelerator
var gpu = new CudaAccelerator(new Context());
//Create typed launcher
var matrixMulKernel = gpu.LoadAutoGroupedStreamKernel <
Index2,
ArrayView <float>,
ArrayView <float>,
ArrayView <float>,
int>(MatrixMul);
//Allocate memory
int buffSize = N * N;
MemoryBuffer <float> d_a = gpu.Allocate <float>(buffSize);
MemoryBuffer <float> d_b = gpu.Allocate <float>(buffSize);
MemoryBuffer <float> d_c = gpu.Allocate <float>(buffSize);
d_a.CopyFrom(FlatternArr(a), 0, Index1.Zero, buffSize);
d_b.CopyFrom(FlatternArr(b), 0, Index1.Zero, buffSize);
matrixMulKernel(new Index2(N, N), d_a.View, d_b.View, d_c.View, N);
// Wait for the kernel to finish...
gpu.Synchronize();
var c = d_c.GetAsArray();
return(c);
}
public override void Init()
{
GeneratePTX(); // alternative approach through this?
context = new Context();
accelerator = new CudaAccelerator(context);
var methodInfo = typeof(Impl_ILGPU).GetMethod(nameof(MyKernel), BindingFlags.Public | BindingFlags.Static);
myKernel = accelerator.LoadAutoGroupedStreamKernel <Index,
ArrayView <byte>,
ArrayView <double>,
ArrayView <int>,
ArrayView <int>,
ArrayView <double>,
ArrayView <byte>,
int
>(MyKernel);
// Allocate some memory
input1_dev = accelerator.Allocate <int>(DataGenerator.In1.Length);
input2_dev = accelerator.Allocate <int>(DataGenerator.In2.Length);
input3_dev = accelerator.Allocate <double>(DataGenerator.In3.Length);
input4_dev = accelerator.Allocate <byte>(DataGenerator.In4_3_bytes.Length);
// init output parameters
result_dev = accelerator.Allocate <byte>(resultsBytes.Length);
resultCalc_dev = accelerator.Allocate <double>(calculatables.Length);
input1_dev.CopyFrom(DataGenerator.In1, 0, 0, DataGenerator.In1.Length);
input2_dev.CopyFrom(DataGenerator.In2, 0, 0, DataGenerator.In2.Length);
input3_dev.CopyFrom(DataGenerator.In3, 0, 0, DataGenerator.In3.Length);
input4_dev.CopyFrom(DataGenerator.In4_3_bytes, 0, 0, DataGenerator.In4_3_bytes.Length);
}
public static float[] RunOddEvenSort2(float[] a)
{
int N = a.Length;
bool evenArr = (N % 2) == 0 ? true : false;
//Create context and accelerator
var gpu = new CudaAccelerator(new Context());
//Create typed launcher
var oddEvenKernel = gpu.LoadAutoGroupedStreamKernel <
Index1,
ArrayView <float>,
VariableView <byte>,
int,
bool>(OddEvenSort2);
//Allocate memory
MemoryBuffer <float> d_a = gpu.Allocate <float>(N);
MemoryBuffer <byte> d_stopFlag = gpu.AllocateZero <byte>(1);
d_a.CopyFrom(a, 0, Index1.Zero, N);
//Run kernel
oddEvenKernel(N / 2, d_a, d_stopFlag.View.GetVariableView(0), N, evenArr);
gpu.Synchronize();
return(d_a.GetAsArray());
}
public static float[] RunFloydWarshall(float[][] a, int N, ref Stopwatch sw)
{
//Create context and accelerator
var gpu = new CudaAccelerator(new Context());
//Create typed launcher
var floydWarshallKernel = gpu.LoadAutoGroupedStreamKernel <
Index1,
int,
ArrayView <float>,
int>(FloydWarshall);
//Allocate memory
var bufSize = N * N;
MemoryBuffer <float> d_graphMinDist = gpu.Allocate <float>(bufSize);
d_graphMinDist.CopyFrom(FlatternArr(a), 0, Index1.Zero, bufSize);
sw.Restart();
for (int k = 0; k < N; k++)
{
floydWarshallKernel(bufSize, k, d_graphMinDist, N);
gpu.Synchronize();
}
sw.Stop();
return(d_graphMinDist.GetAsArray());
}
public static void Main()
{
using Context context = new Context();
context.EnableAlgorithms();
using Accelerator device = new CudaAccelerator(context);
int width = 1920;
int height = 1080;
byte[] h_bitmapData = new byte[width * height * 3];
using MemoryBuffer2D <Vec3> canvasData = device.Allocate <Vec3>(width, height);
using MemoryBuffer <byte> d_bitmapData = device.Allocate <byte>(width * height * 3);
CanvasData c = new CanvasData(canvasData, d_bitmapData, width, height);
// pos // look at // up
Camera camera = new Camera(new Vec3(0, 50, -100), new Vec3(0, 0, 0), new Vec3(0, -1, 0), width, height, 40f);
WorldData world = new WorldData(device);
//world.loadMesh(new Vec3(10, 0, 0), "./Assets/defaultcube.obj");
world.loadMesh(new Vec3(0, 0, 0), "./Assets/cat.obj");
var frameBufferToBitmap = device.LoadAutoGroupedStreamKernel <Index2, CanvasData>(CanvasData.CanvasToBitmap);
var RTMethod = device.LoadAutoGroupedStreamKernel <Index2, CanvasData, dWorldBuffer, Camera>(PerPixelRayIntersectionMethod);
//do rasterization here
Stopwatch timer = new Stopwatch();
timer.Start();
RTMethod(new Index2(width, height), c, world.getDeviceWorldBuffer(), camera);
frameBufferToBitmap(canvasData.Extent, c);
device.Synchronize();
d_bitmapData.CopyTo(h_bitmapData, 0, 0, d_bitmapData.Extent);
timer.Stop();
Console.WriteLine("Rendered in: " + timer.Elapsed);
//bitmap magic that ignores striding be careful with some
using Bitmap b = new Bitmap(width, height, width * 3, PixelFormat.Format24bppRgb, Marshal.UnsafeAddrOfPinnedArrayElement(h_bitmapData, 0));
b.Save("out.bmp");
Process.Start("cmd.exe", "/c out.bmp");
}
public static float[] RunOddEvenSort(float[] a, ref Stopwatch sw)
{
int N = a.Length;
bool evenArr = (N % 2) == 0 ? true : false;
bool stopFlag = false;
bool iterationEven = true;
//Create context and accelerator
var gpu = new CudaAccelerator(new Context());
//Create typed launcher
var oddEvenKernel = gpu.LoadAutoGroupedStreamKernel <
Index1,
ArrayView <float>,
VariableView <byte>,
bool,
int,
bool>(OddEvenSort);
//Allocate memory
MemoryBuffer <float> d_a = gpu.Allocate <float>(N);
MemoryBuffer <byte> d_stopFlag = gpu.AllocateZero <byte>(1);
d_a.CopyFrom(a, 0, Index1.Zero, N);
sw.Restart();
//Run kernel
byte[] zero_val = new byte[1];
zero_val[0] = 0;
while (true)
{
if (stopFlag)
{
break;
}
stopFlag = true;
d_stopFlag.CopyFrom(zero_val, 0, 0, 1);
oddEvenKernel(N / 2, d_a, d_stopFlag.View.GetVariableView(), iterationEven, N, evenArr);
gpu.Synchronize();
if (d_stopFlag.GetAsArray()[0] > 0)
{
stopFlag = false;
}
iterationEven = !iterationEven;
}
sw.Stop();
return(d_a.GetAsArray());
}
/// <summary>
/// Demonstrates a block statement, with local register declaration.
/// </summary>
static void AddUsingTempRegister(CudaAccelerator accelerator)
{
using var buffer = accelerator.Allocate1D <double>(1024);
var kernel = accelerator.LoadAutoGroupedStreamKernel <Index1D, ArrayView <double> >(MultipleInstructionKernel);
kernel((int)buffer.Length, buffer.View);
var results = buffer.GetAsArray1D();
for (var i = 0; i < results.Length; i++)
{
Console.WriteLine($"[{i}] = {results[i]}");
}
}
/// <summary>
/// Demonstrates using EmitRef.
/// </summary>
static void SubtractUsingEmitRef(CudaAccelerator accelerator)
{
using var buffer = accelerator.Allocate1D <long>(32);
var kernel = accelerator.LoadAutoGroupedStreamKernel <Index1D, ArrayView <long> >(SubtractEmitRefKernel);
kernel((int)buffer.Length, buffer.View);
var results = buffer.GetAsArray1D();
for (var i = 0; i < results.Length; i++)
{
Console.WriteLine($"[{i}] = {results[i]}");
}
}
/// <summary>
/// Demonstrates using the mul.hi.u64 and mul.lo.u64 inline PTX instructions to
/// multiply two UInt64 values to produce a UInt128 value.
/// </summary>
static void MultiplyUInt128(CudaAccelerator accelerator)
{
using var buffer = accelerator.Allocate1D <UInt128>(1024);
var kernel = accelerator.LoadAutoGroupedStreamKernel <Index1D, ArrayView <UInt128>, SpecializedValue <ulong> >(MultiplyUInt128Kernel);
kernel(
(int)buffer.Length,
buffer.View,
SpecializedValue.New(ulong.MaxValue));
var results = buffer.GetAsArray1D();
for (var i = 0; i < results.Length; i++)
{
Console.WriteLine($"[{i}] = {results[i]}");
}
}
private static void Performance()
{
using (var context = new Context())
{
using (var accelerator = new CudaAccelerator(context))
{
using (var b = accelerator.CreateBackend())
{
using (var c = accelerator.Context.CreateCompileUnit(b))
{
var method = typeof(Program).GetMethod("MathKernel", BindingFlags.Static | BindingFlags.Public);
var compiled = b.Compile(c, method);
var kernel = accelerator.LoadAutoGroupedStreamKernel<Index2, ArrayView2D<float>>(MathKernel);
//var kernel = accelerator.LoadAutoGroupedKernel(compiled);
int size = 100000;
var W = new[] { 50 };
var H = new[] { 50 };
for (int n = 0; n < W.Length; n++)
{
for (int m = 0; m < H.Length; m++)
{
int x = W[n];
int y = H[m];
Console.WriteLine($"\n\nW {x}, H {y} \n\n");
//var watch = Stopwatch.StartNew();
//for (int k = 0; k < size; k++)
//{
// var v = new float[x, y];
// for (int i = 0; i < x; i++)
// {
// for (int j = 0; j < y; j++)
// {
// v[i, j] = (float)Math.Sqrt(i * j);
// }
// }
//}
//watch.Stop();
//Console.WriteLine($"\n\nElapsed CPU Time Linear: {watch.ElapsedMilliseconds}ms\n");
//GC.Collect();
//
//watch = Stopwatch.StartNew();
//Parallel.For(0, size, k =>
//{
// var v = new float[x, y];
// Parallel.For(0, x, i =>
// {
// Parallel.For(0, y, j =>
// {
// v[i, j] = (float)Math.Sqrt(i * j);
// });
// });
//});
//watch.Stop();
//Console.WriteLine($"Elapsed CPU Time Parallel: {watch.ElapsedMilliseconds}ms\n\n");
//GC.Collect();
//var watch = Stopwatch.StartNew();
//for (int k = 0; k < size; k++)
//{
// var idx = new Index2(x, y);
// var buffer = accelerator.Allocate<float>(idx);
// kernel(idx, buffer.View);
// accelerator.Synchronize();
// buffer.Dispose();
//}
//watch.Stop();
//Console.WriteLine($"\n\nElapsed GPU Time Linear: {watch.ElapsedMilliseconds}ms\n");
//GC.Collect();
var kn = Enumerable.Repeat(accelerator.LoadAutoGroupedStreamKernel<Index2, ArrayView2D<float>>(MathKernel), size).ToList();
var watch = Stopwatch.StartNew();
Parallel.For(0, size, k =>
{
var idx = new Index2(x, y);
var buffer = accelerator.Allocate<float>(idx);
//kn[k](idx, buffer.View);
//kernel.Launch(idx, buffer.View);
kernel(idx, buffer.View);
accelerator.Synchronize();
buffer.Dispose();
});
watch.Stop();
Console.WriteLine($"Elapsed GPU Time Parallel: {watch.ElapsedMilliseconds}ms\n\n");
GC.Collect();
}
}
}
}
}
}
}
public void ProccessOld()
{
// Create the required ILGPU context
using (var context = new Context())
{
/*
* using (var accelerator = new CPUAccelerator(context))
* {
* // accelerator.LoadAutoGroupedStreamKernel creates a typed launcher
* // that implicitly uses the default accelerator stream.
* // In order to create a launcher that receives a custom accelerator stream
* // use: accelerator.LoadAutoGroupedKernel<Index, ArrayView<int> int>(...)
* var myKernel = accelerator.LoadAutoGroupedStreamKernel<Index, ArrayView<int>, int>(MyKernel2);
*
* // Allocate some memory
* using (var buffer = accelerator.Allocate<int>(1024))
* {
* // Launch buffer.Length many threads and pass a view to buffer
* myKernel(buffer.Length, buffer.View, 42);
*
* // Wait for the kernel to finish...
* accelerator.Synchronize();
*
* // Resolve data
* var data = buffer.GetAsArray();
* // ...
* }
* }*/
using (var accelerator = new CudaAccelerator(context)) // test with CPUAccelerator
{
var methodInfo = typeof(Impl_ILGPU).GetMethod(nameof(MyKernel), BindingFlags.Public | BindingFlags.Static);
var myKernel = accelerator.LoadAutoGroupedStreamKernel <Index,
ArrayView <byte>,
ArrayView <double>,
ArrayView <int>,
ArrayView <int>,
ArrayView <double>,
ArrayView <byte>,
int
>(MyKernel);
/*
* var myKernel = accelerator.LoadAutoGroupedStreamKernel<Action<Index,
* ArrayView<byte>,
* ArrayView<int>,
* ArrayView<int>,
* ArrayView<double>,
* ArrayView2D<byte>>>(methodInfo);*/
// Allocate some memory
var input1_dev = accelerator.Allocate <int>(DataGenerator.In1.Length);
var input2_dev = accelerator.Allocate <int>(DataGenerator.In2.Length);
var input3_dev = accelerator.Allocate <double>(DataGenerator.In3.Length);
//var input4_dev = accelerator.Allocate<byte>(DataGenerator.In4_2.GetLength(0), DataGenerator.In4_2.GetLength(1));
var input4_dev = accelerator.Allocate <byte>(DataGenerator.In4_3_bytes.Length);
// init output parameters
var result_dev = accelerator.Allocate <byte>(resultsBytes.Length);
var resultCalc_dev = accelerator.Allocate <double>(calculatables.Length);
input1_dev.CopyFrom(DataGenerator.In1, 0, 0, DataGenerator.In1.Length);
input2_dev.CopyFrom(DataGenerator.In2, 0, 0, DataGenerator.In2.Length);
input3_dev.CopyFrom(DataGenerator.In3, 0, 0, DataGenerator.In3.Length);
//input4_dev.CopyFrom(DataFeeder.In4_2_bytes, new Index2(), new Index2(DataFeeder.In4_2_bytes.GetLength(0), 0), new Index2(1, 2));
//input4_dev.CopyFrom(DataGenerator.In4_2_bytes, Index2.Zero, Index2.Zero, input4_dev.Extent);
input4_dev.CopyFrom(DataGenerator.In4_3_bytes, 0, 0, DataGenerator.In4_3_bytes.Length);
myKernel(input1_dev.Length, result_dev.View, resultCalc_dev.View, input1_dev.View, input2_dev.View, input3_dev.View, input4_dev.View, DataGenerator.Width);
// Wait for the kernel to finish...
accelerator.Synchronize();
// Resolve data
resultsBytes = result_dev.GetAsArray();
calculatables = resultCalc_dev.GetAsArray();
//d_in1.Dispose();
//d_in1 = null;
/*
* var kernelWithDefaultStream = accelerator.LoadAutoGroupedStreamKernel<
* Index,
* ArrayView<bool>,
* ArrayView<int>,
* ArrayView<int>,
* ArrayView<double>,
* ArrayView2D<bool>
* >(MyKernel);
*
* kernelWithDefaultStream(buffer.Extent, buffer.View, 1);
*/
// Launch buffer.Length many threads and pass a view to buffer
//myKernel(d_in1.Length, d_in1.View, 42);
// Wait for the kernel to finish...
//accelerator.Synchronize();
// Resolve data
//var data = buffer.GetAsArray();
// ...
}
}
}