/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *  * Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 *  * Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the distribution.
 *  * Neither the name of NVIDIA CORPORATION nor the names of its
 *    contributors may be used to endorse or promote products derived
 *    from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
 * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
 * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
 * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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 */

/*
* This sample demonstrates how use texture fetches in CUDA
*
* This sample takes an input PGM image (image_filename) and generates
* an output PGM image (image_filename_out).  This CUDA kernel performs
* a simple 2D transform (rotation) on the texture coordinates (u,v).
* The results between simpleTexture and simpleTextureDrv are identical.
* The main difference is the implementation.  simpleTextureDrv makes calls
* to the CUDA driver API and demonstrates how to use cuModuleLoad to load
* the CUDA ptx (*.ptx) kernel just prior to kernel launch.
*
*/

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

// includes, CUDA
#include <cuda.h>
#include <builtin_types.h>
// includes, project
#include <helper_cuda_drvapi.h>
#include <helper_functions.h>

using namespace std;

const char *image_filename = "lena_bw.pgm";
const char *ref_filename = "ref_rotated.pgm";
float angle = 0.5f;  // angle to rotate image by (in radians)

#define MIN_EPSILON_ERROR 5e-3f

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

extern "C" void computeGold(float *reference, float *idata,
                            const unsigned int len);

static CUresult initCUDA(int argc, char **argv, CUfunction *);

const char *sSDKsample = "simpleTextureDrv (Driver API)";

// define input fatbin file
#ifndef FATBIN_FILE
#define FATBIN_FILE "simpleTexture_kernel64.fatbin"
#endif

////////////////////////////////////////////////////////////////////////////////
// Globals
////////////////////////////////////////////////////////////////////////////////
CUdevice cuDevice;
CUcontext cuContext;
CUmodule cuModule;

void showHelp() {
  printf("\n> [%s] Command line options\n", sSDKsample);
  printf("\t-device=n          (where n=0,1,2.... for the GPU device)\n\n");
}

////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
  if (checkCmdLineFlag(argc, (const char **)argv, "help")) {
    showHelp();
    return 0;
  }

  runTest(argc, argv);
}

////////////////////////////////////////////////////////////////////////////////
//! Run a simple test for CUDA
////////////////////////////////////////////////////////////////////////////////
void runTest(int argc, char **argv) {
  bool bTestResults = true;

  // initialize CUDA
  CUfunction transform = NULL;

  if (initCUDA(argc, argv, &transform) != CUDA_SUCCESS) {
    exit(EXIT_FAILURE);
  }

  // load image from disk
  float *h_data = NULL;
  unsigned int width, height;
  char *image_path = sdkFindFilePath(image_filename, argv[0]);

  if (image_path == NULL) {
    printf("Unable to find image file: '%s'\n", image_filename);
    exit(EXIT_FAILURE);
  }

  sdkLoadPGM(image_path, &h_data, &width, &height);

  size_t size = width * height * sizeof(float);
  printf("Loaded '%s', %d x %d pixels\n", image_filename, width, height);

  // load reference image from image (output)
  float *h_data_ref = (float *)malloc(size);
  char *ref_path = sdkFindFilePath(ref_filename, argv[0]);

  if (ref_path == NULL) {
    printf("Unable to find reference file %s\n", ref_filename);
    exit(EXIT_FAILURE);
  }

  sdkLoadPGM(ref_path, &h_data_ref, &width, &height);

  // allocate device memory for result
  CUdeviceptr d_data = (CUdeviceptr)NULL;
  checkCudaErrors(cuMemAlloc(&d_data, size));

  // allocate array and copy image data
  CUarray cu_array;
  CUDA_ARRAY_DESCRIPTOR desc;
  desc.Format = CU_AD_FORMAT_FLOAT;
  desc.NumChannels = 1;
  desc.Width = width;
  desc.Height = height;
  checkCudaErrors(cuArrayCreate(&cu_array, &desc));
  CUDA_MEMCPY2D copyParam;
  memset(&copyParam, 0, sizeof(copyParam));
  copyParam.dstMemoryType = CU_MEMORYTYPE_ARRAY;
  copyParam.dstArray = cu_array;
  copyParam.srcMemoryType = CU_MEMORYTYPE_HOST;
  copyParam.srcHost = h_data;
  copyParam.srcPitch = width * sizeof(float);
  copyParam.WidthInBytes = copyParam.srcPitch;
  copyParam.Height = height;
  checkCudaErrors(cuMemcpy2D(&copyParam));

  // set texture parameters
  CUtexObject TexObject;
  CUDA_RESOURCE_DESC ResDesc;
  memset(&ResDesc, 0, sizeof(CUDA_RESOURCE_DESC));
  ResDesc.resType = CU_RESOURCE_TYPE_ARRAY;
  ResDesc.res.array.hArray = cu_array;

  CUDA_TEXTURE_DESC TexDesc;
  memset(&TexDesc, 0, sizeof(CUDA_TEXTURE_DESC));
  TexDesc.addressMode[0] = CU_TR_ADDRESS_MODE_WRAP;
  TexDesc.addressMode[1] = CU_TR_ADDRESS_MODE_WRAP;
  TexDesc.addressMode[2] = CU_TR_ADDRESS_MODE_WRAP;
  TexDesc.filterMode = CU_TR_FILTER_MODE_LINEAR;
  TexDesc.flags = CU_TRSF_NORMALIZED_COORDINATES;

  checkCudaErrors(cuTexObjectCreate(&TexObject, &ResDesc, &TexDesc, NULL));

  // There are two ways to launch CUDA kernels via the Driver API.
  // In this CUDA Sample, we illustrate both ways to pass parameters
  // and specify parameters.  By default we use the simpler method.
  int block_size = 8;
  StopWatchInterface *timer = NULL;

  if (1) {
    // This is the new CUDA 4.0 API for Kernel Parameter passing and Kernel
    // Launching (simpler method)
    void *args[5] = {&d_data, &width, &height, &angle, &TexObject};

    checkCudaErrors(cuLaunchKernel(transform, (width / block_size),
                                   (height / block_size), 1, block_size,
                                   block_size, 1, 0, NULL, args, NULL));
    checkCudaErrors(cuCtxSynchronize());
    sdkCreateTimer(&timer);
    sdkStartTimer(&timer);

    // launch kernel again for performance measurement
    checkCudaErrors(cuLaunchKernel(transform, (width / block_size),
                                   (height / block_size), 1, block_size,
                                   block_size, 1, 0, NULL, args, NULL));
  } else {
    // This is the new CUDA 4.0 API for Kernel Parameter passing and Kernel
    // Launching (advanced method)
    int offset = 0;
    char argBuffer[256];

    // pass in launch parameters (not actually de-referencing CUdeviceptr).
    // CUdeviceptr is
    // storing the value of the parameters
    *((CUdeviceptr *)&argBuffer[offset]) = d_data;
    offset += sizeof(d_data);
    *((unsigned int *)&argBuffer[offset]) = width;
    offset += sizeof(width);
    *((unsigned int *)&argBuffer[offset]) = height;
    offset += sizeof(height);
    *((float *)&argBuffer[offset]) = angle;
    offset += sizeof(angle);
    *((CUtexObject *)&argBuffer[offset]) = TexObject;
    offset += sizeof(TexObject);

    void *kernel_launch_config[5] = {CU_LAUNCH_PARAM_BUFFER_POINTER, argBuffer,
                                     CU_LAUNCH_PARAM_BUFFER_SIZE, &offset,
                                     CU_LAUNCH_PARAM_END};

    // new CUDA 4.0 Driver API Kernel launch call (warmup)
    checkCudaErrors(cuLaunchKernel(
        transform, (width / block_size), (height / block_size), 1, block_size,
        block_size, 1, 0, NULL, NULL, (void **)&kernel_launch_config));
    checkCudaErrors(cuCtxSynchronize());
    sdkCreateTimer(&timer);
    sdkStartTimer(&timer);

    // launch kernel again for performance measurement
    checkCudaErrors(cuLaunchKernel(
        transform, (width / block_size), (height / block_size), 1, block_size,
        block_size, 1, 0, 0, NULL, (void **)&kernel_launch_config));
  }

  checkCudaErrors(cuCtxSynchronize());
  sdkStopTimer(&timer);
  printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer));
  printf("%.2f Mpixels/sec\n",
         (width * height / (sdkGetTimerValue(&timer) / 1000.0f)) / 1e6);
  sdkDeleteTimer(&timer);

  // allocate mem for the result on host side
  float *h_odata = (float *)malloc(size);
  // copy result from device to host
  checkCudaErrors(cuMemcpyDtoH(h_odata, d_data, size));

  // write result to file
  char output_filename[1024];
  strcpy(output_filename, image_path);
  strcpy(output_filename + strlen(image_path) - 4, "_out.pgm");
  sdkSavePGM(output_filename, h_odata, width, height);
  printf("Wrote '%s'\n", output_filename);

  // write regression file if necessary
  if (checkCmdLineFlag(argc, (const char **)argv, "regression")) {
    // write file for regression test
    sdkWriteFile<float>("./data/regression.dat", h_odata, width * height, 0.0f,
                        false);
  } else {
    // We need to reload the data from disk, because it is inverted upon output
    sdkLoadPGM(output_filename, &h_odata, &width, &height);

    printf("Comparing files\n");
    printf("\toutput:    <%s>\n", output_filename);
    printf("\treference: <%s>\n", ref_path);
    bTestResults = compareData(h_odata, h_data_ref, width * height,
                               MIN_EPSILON_ERROR, 0.15f);
  }

  // cleanup memory
  checkCudaErrors(cuTexObjectDestroy(TexObject));
  checkCudaErrors(cuMemFree(d_data));
  checkCudaErrors(cuArrayDestroy(cu_array));

  free(image_path);
  free(ref_path);

  checkCudaErrors(cuCtxDestroy(cuContext));

  exit(bTestResults ? EXIT_SUCCESS : EXIT_FAILURE);
}

////////////////////////////////////////////////////////////////////////////////
//! This initializes CUDA, and loads the *.ptx CUDA module containing the
//! kernel function.  After the module is loaded, cuModuleGetFunction
//! retrieves the CUDA function pointer "cuFunction"
////////////////////////////////////////////////////////////////////////////////
static CUresult initCUDA(int argc, char **argv, CUfunction *transform) {
  CUfunction cuFunction = 0;
  int major = 0, minor = 0, devID = 0;
  char deviceName[100];
  string module_path;

  cuDevice = findCudaDeviceDRV(argc, (const char **)argv);

  // get compute capabilities and the devicename
  checkCudaErrors(cuDeviceGetAttribute(
      &major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice));
  checkCudaErrors(cuDeviceGetAttribute(
      &minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice));
  checkCudaErrors(cuDeviceGetName(deviceName, sizeof(deviceName), cuDevice));
  printf("> GPU Device has SM %d.%d compute capability\n", major, minor);

  checkCudaErrors(cuCtxCreate(&cuContext, 0, cuDevice));

  // first search for the module_path before we try to load the results
  std::ostringstream fatbin;

  if (!findFatbinPath(FATBIN_FILE, module_path, argv, fatbin)) {
    exit(EXIT_FAILURE);
  } else {
    printf("> initCUDA loading module: <%s>\n", module_path.c_str());
  }

  if (!fatbin.str().size()) {
    printf("fatbin file empty. exiting..\n");
    exit(EXIT_FAILURE);
  }

  // Create module from binary file (FATBIN)
  checkCudaErrors(cuModuleLoadData(&cuModule, fatbin.str().c_str()));

  checkCudaErrors(
      cuModuleGetFunction(&cuFunction, cuModule, "transformKernel"));

  *transform = cuFunction;

  return CUDA_SUCCESS;
}
