private static bool cutCompareL2fe(Float2[] reference, Float2[] data,
int len, float epsilon)
{
float error = 0;
float reff = 0;
for (int i = 0; i < len / 2; ++i)
{
float diff = reference[i].x - data[i].x;
error += diff * diff;
reff += reference[i].x * reference[i].x;
diff = reference[i].y - data[i].y;
error += diff * diff;
reff += reference[i].y * reference[i].y;
}
float normRef = (float)Math.Sqrt(reff);
if (Math.Abs(reff) < 1e-7)
{
Console.WriteLine("ERROR, reference l2-norm is 0");
return false;
}
float normError = (float)Math.Sqrt(error);
error = normError / normRef;
bool result = error < epsilon;
if (!result)
{
Console.WriteLine("ERROR, l2-norm error {0} is greater than epsilon {1}", error, epsilon);
}
return result ? true : false;
}
static void Main(string[] args)
{
// Init and select 1st device.
CUDA cuda = new CUDA(0, true);
// load module
//cuda.LoadModule(Path.Combine(Environment.CurrentDirectory, "simpleCUFFT.ptx"));
CUfunction func = new CUfunction();// cuda.GetModuleFunction("ComplexPointwiseMulAndScale");
// The filter size is assumed to be a number smaller than the signal size
const int SIGNAL_SIZE = 50;
const int FILTER_KERNEL_SIZE = 11;
// Allocate host memory for the signal
Float2[] h_signal = new Float2[SIGNAL_SIZE];
// Initalize the memory for the signal
Random r = new Random();
for (int i = 0; i < SIGNAL_SIZE; ++i)
{
h_signal[i].x = r.Next() / (float)int.MaxValue;
h_signal[i].y = 0;
}
// Allocate host memory for the filter
Float2[] h_filter_kernel = new Float2[FILTER_KERNEL_SIZE];
// Initalize the memory for the filter
for (int i = 0; i < FILTER_KERNEL_SIZE; ++i)
{
h_filter_kernel[i].x = r.Next() / (float)int.MaxValue;
h_filter_kernel[i].y = 0;
}
// Pad signal and filter kernel
Float2[] h_padded_signal;
Float2[] h_padded_filter_kernel;
int new_size = PadData(h_signal, out h_padded_signal, SIGNAL_SIZE,
h_filter_kernel, out h_padded_filter_kernel, FILTER_KERNEL_SIZE);
// Allocate device memory for signal
// Copy host memory to device
CUdeviceptr d_signal = cuda.CopyHostToDevice<Float2>(h_padded_signal);
// Allocate device memory for filter kernel
// Copy host memory to device
CUdeviceptr d_filter_kernel = cuda.CopyHostToDevice<Float2>(h_padded_filter_kernel);
// CUFFT plan
CUFFT fft = new CUFFT(cuda);
cufftHandle handle = new cufftHandle();
CUFFTResult fftres = CUFFTDriver.cufftPlan1d(ref handle, new_size, CUFFTType.C2C, 1);
//fft.Plan1D(new_size, CUFFTType.C2C, 1);
return;
// Transform signal and kernel
fft.ExecuteComplexToComplex(d_signal, d_signal, CUFFTDirection.Forward);
fft.ExecuteComplexToComplex(d_filter_kernel, d_filter_kernel, CUFFTDirection.Forward);
// Multiply the coefficients together and normalize the result
// ComplexPointwiseMulAndScale<<<32, 256>>>(d_signal, d_filter_kernel, new_size, 1.0f / new_size);
cuda.SetFunctionBlockShape(func, 256, 1, 1);
cuda.SetParameter(func, 0, (uint)d_signal.Pointer);
cuda.SetParameter(func, IntPtr.Size, (uint)d_filter_kernel.Pointer);
cuda.SetParameter(func, IntPtr.Size * 2, (uint)new_size);
cuda.SetParameter(func, IntPtr.Size * 2 + 4, 1.0f / new_size);
cuda.SetParameterSize(func, (uint)(IntPtr.Size * 2 + 8));
cuda.Launch(func, 32, 1);
// Transform signal back
fft.ExecuteComplexToComplex(d_signal, d_signal, CUFFTDirection.Inverse);
// Copy device memory to host
Float2[] h_convolved_signal = h_padded_signal;
cuda.CopyDeviceToHost<Float2>(d_signal, h_convolved_signal);
// Allocate host memory for the convolution result
Float2[] h_convolved_signal_ref = new Float2[SIGNAL_SIZE];
// Convolve on the host
Convolve(h_signal, SIGNAL_SIZE,
h_filter_kernel, FILTER_KERNEL_SIZE,
h_convolved_signal_ref);
// check result
bool res = cutCompareL2fe(h_convolved_signal_ref, h_convolved_signal, 2 * SIGNAL_SIZE, 1e-5f);
Console.WriteLine("Test {0}", (true == res) ? "PASSED" : "FAILED");
//Destroy CUFFT context
fft.Destroy();
// cleanup memory
cuda.Free(d_signal);
cuda.Free(d_filter_kernel);
}
private static Float2 ComplexMul(Float2 a, Float2 b)
{
Float2 res = new Float2();
res.x = a.x * b.x - a.y * b.y;
res.y = a.x * b.y + a.y * b.x;
return res;
}
private static Float2 ComplexAdd(Float2 n1, Float2 n2)
{
Float2 res = new Float2();
res.x = n1.x + n2.x;
res.y = n1.y + n2.y;
return res;
}
////////////////////////////////////////////////////////////////////////////////
// Filtering operations
////////////////////////////////////////////////////////////////////////////////
// Computes convolution on the host
private static void Convolve(Float2[] signal, int signal_size,
Float2[] filter_kernel, int filter_kernel_size,
Float2[] filtered_signal)
{
int minRadius = filter_kernel_size / 2;
int maxRadius = filter_kernel_size - minRadius;
// Loop over output element indices
for (int i = 0; i < signal_size; ++i)
{
filtered_signal[i].x = filtered_signal[i].y = 0;
// Loop over convolution indices
for (int j = -maxRadius + 1; j <= minRadius; ++j)
{
int k = i + j;
if (k >= 0 && k < signal_size)
filtered_signal[i] = ComplexAdd(filtered_signal[i], ComplexMul(signal[k], filter_kernel[minRadius - j]));
}
}
}
// Pad data
private static int PadData(Float2[] signal, out Float2[] padded_signal, int signal_size,
Float2[] filter_kernel, out Float2[] padded_filter_kernel, int filter_kernel_size)
{
int minRadius = filter_kernel_size / 2;
int maxRadius = filter_kernel_size - minRadius;
int new_size = signal_size + maxRadius;
// Pad signal
Float2[] new_data = new Float2[new_size];
Array.Copy(signal, new_data, signal_size);
for (int i = 0; i < (new_size - signal_size)/2; i++)
{
new_data[signal_size + i] = new Float2();
}
padded_signal = new_data;
// Pad filter
new_data = new Float2[new_size];
Array.Copy(filter_kernel, minRadius, new_data, 0, maxRadius);
for (int i = 0; i < (new_size - filter_kernel_size)/2; i++)
{
new_data[maxRadius/2 + i] = new Float2();
}
Array.Copy(filter_kernel, (new_size - minRadius) / Marshal.SizeOf(typeof(Float2)), filter_kernel, 0, minRadius);
padded_filter_kernel = new_data;
return new_size;
}
public static extern CUResult cuParamSetv(CUfunction hfunc, int offset, ref Float2 ptr, uint numbytes);
