/*
 * Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
 *
 * Please refer to the NVIDIA end user license agreement (EULA) associated
 * with this source code for terms and conditions that govern your use of
 * this software. Any use, reproduction, disclosure, or distribution of
 * this software and related documentation outside the terms of the EULA
 * is strictly prohibited.
 *
 */

/*
* Copyright 1993-2014 NVIDIA Corporation.  All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*
*/

/* Example showing the use of CUFFT for fast 1D-convolution using FFT. */

// includes, system
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>

// includes, project
#include <cuda_runtime.h>
#include <cufft.h>
#include <cufftXt.h>
#include <helper_functions.h>
#include <helper_cuda.h>

// Complex data type
typedef float2 Complex;
static __device__ __host__ inline Complex ComplexAdd(Complex, Complex);
static __device__ __host__ inline Complex ComplexScale(Complex, float);
static __device__ __host__ inline Complex ComplexMul(Complex, Complex);
static __global__ void ComplexPointwiseMulAndScale(Complex *, const Complex *, int, float);

// Filtering functions
void Convolve(const Complex *, int, const Complex *, int, Complex *);

// Padding functions
int PadData(const Complex *, Complex **, int,
            const Complex *, Complex **, int);

////////////////////////////////////////////////////////////////////////////////
// declaration, forward
void runTest(int argc, char **argv);

// The filter size is assumed to be a number smaller than the signal size
#define SIGNAL_SIZE        50
#define FILTER_KERNEL_SIZE 11

////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv)
{
    runTest(argc, argv);
}

////////////////////////////////////////////////////////////////////////////////
//! Run a simple test for CUDA
////////////////////////////////////////////////////////////////////////////////
void runTest(int argc, char **argv)
{
    printf("[simpleCUFFT] is starting...\n");

    findCudaDevice(argc, (const char **)argv);

    // Allocate host memory for the signal
    Complex *h_signal = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE);

    // Initialize the memory for the signal
    for (unsigned int i = 0; i < SIGNAL_SIZE; ++i)
    {
        h_signal[i].x = rand() / (float)RAND_MAX;
        h_signal[i].y = 0;
    }

    // Allocate host memory for the filter
    Complex *h_filter_kernel = (Complex *)malloc(sizeof(Complex) * FILTER_KERNEL_SIZE);

    // Initialize the memory for the filter
    for (unsigned int i = 0; i < FILTER_KERNEL_SIZE; ++i)
    {
        h_filter_kernel[i].x = rand() / (float)RAND_MAX;
        h_filter_kernel[i].y = 0;
    }

    // Pad signal and filter kernel
    Complex *h_padded_signal;
    Complex *h_padded_filter_kernel;
    int new_size = PadData(h_signal, &h_padded_signal, SIGNAL_SIZE,
                           h_filter_kernel, &h_padded_filter_kernel, FILTER_KERNEL_SIZE);
    int mem_size = sizeof(Complex) * new_size;

    // Allocate device memory for signal
    Complex *d_signal;
    checkCudaErrors(cudaMalloc((void **)&d_signal, mem_size));
    // Copy host memory to device
    checkCudaErrors(cudaMemcpy(d_signal, h_padded_signal, mem_size,
                               cudaMemcpyHostToDevice));

    // Allocate device memory for filter kernel
    Complex *d_filter_kernel;
    checkCudaErrors(cudaMalloc((void **)&d_filter_kernel, mem_size));

    // Copy host memory to device
    checkCudaErrors(cudaMemcpy(d_filter_kernel, h_padded_filter_kernel, mem_size,
                               cudaMemcpyHostToDevice));

    // CUFFT plan simple API
    cufftHandle plan;
    checkCudaErrors(cufftPlan1d(&plan, new_size, CUFFT_C2C, 1));

    // CUFFT plan advanced API
    cufftHandle plan_adv;
    size_t workSize;
    long long int new_size_long = new_size;

    checkCudaErrors(cufftCreate(&plan_adv));
    checkCudaErrors(cufftXtMakePlanMany(plan_adv, 1, &new_size_long, NULL, 1, 1, CUDA_C_32F, NULL, 1, 1, CUDA_C_32F, 1, &workSize, CUDA_C_32F));
    printf("Temporary buffer size %li bytes\n", workSize);

    // Transform signal and kernel
    printf("Transforming signal cufftExecC2C\n");
    checkCudaErrors(cufftExecC2C(plan, (cufftComplex *)d_signal, (cufftComplex *)d_signal, CUFFT_FORWARD));
    checkCudaErrors(cufftExecC2C(plan_adv, (cufftComplex *)d_filter_kernel, (cufftComplex *)d_filter_kernel, CUFFT_FORWARD));

    // Multiply the coefficients together and normalize the result
    printf("Launching ComplexPointwiseMulAndScale<<< >>>\n");
    ComplexPointwiseMulAndScale<<<32, 256>>>(d_signal, d_filter_kernel, new_size, 1.0f / new_size);

    // Check if kernel execution generated and error
    getLastCudaError("Kernel execution failed [ ComplexPointwiseMulAndScale ]");

    // Transform signal back
    printf("Transforming signal back cufftExecC2C\n");
    checkCudaErrors(cufftExecC2C(plan, (cufftComplex *)d_signal, (cufftComplex *)d_signal, CUFFT_INVERSE));

    // Copy device memory to host
    Complex *h_convolved_signal = h_padded_signal;
    checkCudaErrors(cudaMemcpy(h_convolved_signal, d_signal, mem_size,
                               cudaMemcpyDeviceToHost));

    // Allocate host memory for the convolution result
    Complex *h_convolved_signal_ref = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE);

    // Convolve on the host
    Convolve(h_signal, SIGNAL_SIZE,
             h_filter_kernel, FILTER_KERNEL_SIZE,
             h_convolved_signal_ref);

    // check result
    bool bTestResult = sdkCompareL2fe((float *)h_convolved_signal_ref, (float *)h_convolved_signal, 2 * SIGNAL_SIZE, 1e-5f);

    //Destroy CUFFT context
    checkCudaErrors(cufftDestroy(plan));
    checkCudaErrors(cufftDestroy(plan_adv));

    // cleanup memory
    free(h_signal);
    free(h_filter_kernel);
    free(h_padded_signal);
    free(h_padded_filter_kernel);
    free(h_convolved_signal_ref);
    checkCudaErrors(cudaFree(d_signal));
    checkCudaErrors(cudaFree(d_filter_kernel));

    exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE);
}

// Pad data
int PadData(const Complex *signal, Complex **padded_signal, int signal_size,
            const Complex *filter_kernel, Complex **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
    Complex *new_data = (Complex *)malloc(sizeof(Complex) * new_size);
    memcpy(new_data +           0, signal,              signal_size * sizeof(Complex));
    memset(new_data + signal_size,      0, (new_size - signal_size) * sizeof(Complex));
    *padded_signal = new_data;

    // Pad filter
    new_data = (Complex *)malloc(sizeof(Complex) * new_size);
    memcpy(new_data +                    0, filter_kernel + minRadius,                       maxRadius * sizeof(Complex));
    memset(new_data +            maxRadius,                         0, (new_size - filter_kernel_size) * sizeof(Complex));
    memcpy(new_data + new_size - minRadius,             filter_kernel,                       minRadius * sizeof(Complex));
    *padded_filter_kernel = new_data;

    return new_size;
}

////////////////////////////////////////////////////////////////////////////////
// Filtering operations
////////////////////////////////////////////////////////////////////////////////

// Computes convolution on the host
void Convolve(const Complex *signal, int signal_size,
              const Complex *filter_kernel, int filter_kernel_size,
              Complex *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]));
            }
        }
    }
}

////////////////////////////////////////////////////////////////////////////////
// Complex operations
////////////////////////////////////////////////////////////////////////////////

// Complex addition
static __device__ __host__ inline Complex ComplexAdd(Complex a, Complex b)
{
    Complex c;
    c.x = a.x + b.x;
    c.y = a.y + b.y;
    return c;
}

// Complex scale
static __device__ __host__ inline Complex ComplexScale(Complex a, float s)
{
    Complex c;
    c.x = s * a.x;
    c.y = s * a.y;
    return c;
}

// Complex multiplication
static __device__ __host__ inline Complex ComplexMul(Complex a, Complex b)
{
    Complex c;
    c.x = a.x * b.x - a.y * b.y;
    c.y = a.x * b.y + a.y * b.x;
    return c;
}

// Complex pointwise multiplication
static __global__ void ComplexPointwiseMulAndScale(Complex *a, const Complex *b, int size, float scale)
{
    const int numThreads = blockDim.x * gridDim.x;
    const int threadID = blockIdx.x * blockDim.x + threadIdx.x;

    for (int i = threadID; i < size; i += numThreads)
    {
        a[i] = ComplexScale(ComplexMul(a[i], b[i]), scale);
    }
}
