/*
 * 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.
 *
 */

/* This sample is a templatized version of the template project.
* It also shows how to correctly templatize dynamically allocated shared
* memory arrays.
* Host code.
*/


// System includes
#include <stdio.h>
#include <assert.h>
#include <string.h>
#include <math.h>

// CUDA runtime
#include <cuda_runtime.h>

// helper functions and utilities to work with CUDA
#include <helper_functions.h>
#include <nvrtc_helper.h>
#include <timer.h>

#ifndef MAX
#define MAX(a,b) (a > b ? a : b)
#endif

int g_TotalFailures = 0;

////////////////////////////////////////////////////////////////////////////////
// declaration, forward

template <class T>
void runTest(int argc, char **argv, int len);

template<class T>
void computeGold(T *reference, T *idata, const unsigned int len)
{
    const T T_len = static_cast<T>(len);

    for (unsigned int i = 0; i < len; ++i)
    {
        reference[i] = idata[i] * T_len;
    }
}


////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////

int main(int argc, char **argv)
{
    printf("> runTest<float,32>\n");

    runTest<float>(argc, argv, 32);

    printf("> runTest<int,64>\n");

    runTest<int>(argc, argv, 64);

    printf("\n[simpleTemplates_nvrtc] -> Test Results: %d Failures\n", g_TotalFailures);

    exit(g_TotalFailures == 0 ? EXIT_SUCCESS : EXIT_FAILURE);
}



// To completely templatize runTest (below) with cutil, we need to use
// template specialization to wrap up CUTIL's array comparison and file writing
// functions for different types.

// Here's the generic wrapper for cutCompare*
template<class T>
class ArrayComparator
{
    public:
        bool compare(const T *reference, T *data, unsigned int len)
        {
            fprintf(stderr, "Error: no comparison function implemented for this type\n");
            return false;
        }
};

// Here's the specialization for ints:
template<>
class ArrayComparator<int>
{
    public:
        bool compare(const int *reference, int *data, unsigned int len)
        {
            return compareData(reference, data, len, 0.15f, 0.0f);
        }
};

// Here's the specialization for floats:
template<>
class ArrayComparator<float>
{
    public:
        bool compare(const float *reference, float *data, unsigned int len)
        {
            return compareData(reference, data, len, 0.15f, 0.15f);
        }
};

// Here's the generic wrapper for cutWriteFile*
template<class T>
class ArrayFileWriter
{
    public:
        bool write(const char *filename, T *data, unsigned int len, float epsilon)
        {
            fprintf(stderr, "Error: no file write function implemented for this type\n");
            return false;
        }
};

// Here's the specialization for ints:
template<>
class ArrayFileWriter<int>
{
    public:
        bool write(const char *filename, int *data, unsigned int len, float epsilon)
        {
            return sdkWriteFile(filename, data, len, epsilon, false);
        }
};

// Here's the specialization for floats:
template<>
class ArrayFileWriter<float>
{
    public:
        bool write(const char *filename, float *data, unsigned int len, float epsilon)
        {
            return sdkWriteFile(filename, data, len, epsilon, false);
        }
};

template <typename T>
CUfunction getKernel(CUmodule in);

template<>
CUfunction getKernel<int>(CUmodule in)
{
    CUfunction kernel_addr;
    checkCudaErrors(cuModuleGetFunction(&kernel_addr, in, "testInt"));

    return kernel_addr;
}

template<>
CUfunction getKernel<float>(CUmodule in)
{
    CUfunction kernel_addr;
    checkCudaErrors(cuModuleGetFunction(&kernel_addr, in, "testFloat"));

    return kernel_addr;
}


////////////////////////////////////////////////////////////////////////////////
//! Run a simple test for CUDA
////////////////////////////////////////////////////////////////////////////////

static bool moduleLoaded = false;
CUmodule module;
char *ptx, *kernel_file;
size_t ptxSize;

template<class T>
void runTest(int argc, char **argv, int len)
{
    if (!moduleLoaded) {
      kernel_file = sdkFindFilePath("simpleTemplates_kernel.cu", argv[0]);
      compileFileToPTX(kernel_file, 0, NULL, &ptx, &ptxSize);
      module = loadPTX(ptx, argc, argv);
      moduleLoaded = true;
    }

    StartTimer();

    unsigned int num_threads = len;
    unsigned int mem_size = sizeof(float) * num_threads;

    // allocate host memory
    T *h_idata = (T *) malloc(mem_size);

    // initialize the memory
    for (unsigned int i = 0; i < num_threads; ++i)
    {
        h_idata[i] = (T) i;
    }

    // allocate device memory
    CUdeviceptr d_idata;
    checkCudaErrors(cuMemAlloc(&d_idata, mem_size));

    // copy host memory to device
    checkCudaErrors(cuMemcpyHtoD(d_idata, h_idata, mem_size));

    // allocate device memory for result
    CUdeviceptr d_odata;
    checkCudaErrors(cuMemAlloc(&d_odata, mem_size));

    // setup execution parameters
    dim3  grid(1, 1, 1);
    dim3  threads(num_threads, 1, 1);

    // execute the kernel
    CUfunction kernel_addr = getKernel<T>(module);

    void *arr[] = { (void *)&d_idata, (void *)&d_odata };
    checkCudaErrors(cuLaunchKernel(kernel_addr,
                                            grid.x, grid.y, grid.z, /* grid dim */
                                            threads.x, threads.y, threads.z, /* block dim */
                                            mem_size,0, /* shared mem, stream */
                                            &arr[0], /* arguments */
                                            0));

    // check if kernel execution generated and error
    checkCudaErrors(cuCtxSynchronize());

    // allocate mem for the result on host side
    T *h_odata = (T *) malloc(mem_size);

    // copy result from device to host
    checkCudaErrors(cuMemcpyDtoH(h_odata, d_odata, sizeof(T) * num_threads));

    printf("Processing time: %f (ms)\n", GetTimer());

    // compute reference solution
    T *reference = (T *) malloc(mem_size);

    computeGold<T>(reference, h_idata, num_threads);

    ArrayComparator<T> comparator;
    ArrayFileWriter<T> writer;

    // check result
    if (checkCmdLineFlag(argc, (const char **) argv, "regression"))
    {
        // write file for regression test
        writer.write("./data/regression.dat", h_odata, num_threads, 0.0f);
    }
    else
    {
        // custom output handling when no regression test running
        // in this case check if the result is equivalent to the expected solution
        bool res = comparator.compare(reference, h_odata, num_threads);

        printf("Compare %s\n\n", (1 == res) ? "OK" : "MISMATCH");

        g_TotalFailures += (1 != res);
    }

    // cleanup memory
    free(h_idata);
    free(h_odata);
    free(reference);
    checkCudaErrors(cuMemFree(d_idata));
    checkCudaErrors(cuMemFree(d_odata));
}
