/* 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
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 */

/*
 * This application demonstrates how to use the CUDA API to use multiple GPUs,
 * with an emphasis on simple illustration of the techniques (not on
 * performance).
 *
 * Note that in order to detect multiple GPUs in your system you have to disable
 * SLI in the nvidia control panel. Otherwise only one GPU is visible to the
 * application. On the other side, you can still extend your desktop to screens
 * attached to both GPUs.
 */

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

// CUDA runtime
#include <cuda_runtime.h>

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

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

#include "simpleMultiGPU.h"

////////////////////////////////////////////////////////////////////////////////
// Data configuration
////////////////////////////////////////////////////////////////////////////////
const int MAX_GPU_COUNT = 32;
const int DATA_N = 1048576 * 32;

////////////////////////////////////////////////////////////////////////////////
// Simple reduction kernel.
// Refer to the 'reduction' CUDA Sample describing
// reduction optimization strategies
////////////////////////////////////////////////////////////////////////////////
__global__ static void reduceKernel(float *d_Result, float *d_Input, int N) {
  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
  const int threadN = gridDim.x * blockDim.x;
  float sum = 0;

  for (int pos = tid; pos < N; pos += threadN) sum += d_Input[pos];

  d_Result[tid] = sum;
}

////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
  // Solver config
  TGPUplan plan[MAX_GPU_COUNT];

  // GPU reduction results
  float h_SumGPU[MAX_GPU_COUNT];

  float sumGPU;
  double sumCPU, diff;

  int i, j, gpuBase, GPU_N;

  const int BLOCK_N = 32;
  const int THREAD_N = 256;
  const int ACCUM_N = BLOCK_N * THREAD_N;

  printf("Starting simpleMultiGPU\n");
  checkCudaErrors(cudaGetDeviceCount(&GPU_N));

  if (GPU_N > MAX_GPU_COUNT) {
    GPU_N = MAX_GPU_COUNT;
  }

  printf("CUDA-capable device count: %i\n", GPU_N);

  printf("Generating input data...\n\n");

  // Subdividing input data across GPUs
  // Get data sizes for each GPU
  for (i = 0; i < GPU_N; i++) {
    plan[i].dataN = DATA_N / GPU_N;
  }

  // Take into account "odd" data sizes
  for (i = 0; i < DATA_N % GPU_N; i++) {
    plan[i].dataN++;
  }

  // Assign data ranges to GPUs
  gpuBase = 0;

  for (i = 0; i < GPU_N; i++) {
    plan[i].h_Sum = h_SumGPU + i;
    gpuBase += plan[i].dataN;
  }

  // Create streams for issuing GPU command asynchronously and allocate memory
  // (GPU and System page-locked)
  for (i = 0; i < GPU_N; i++) {
    checkCudaErrors(cudaSetDevice(i));
    checkCudaErrors(cudaStreamCreate(&plan[i].stream));
    // Allocate memory
    checkCudaErrors(
        cudaMalloc((void **)&plan[i].d_Data, plan[i].dataN * sizeof(float)));
    checkCudaErrors(
        cudaMalloc((void **)&plan[i].d_Sum, ACCUM_N * sizeof(float)));
    checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Sum_from_device,
                                   ACCUM_N * sizeof(float)));
    checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Data,
                                   plan[i].dataN * sizeof(float)));

    for (j = 0; j < plan[i].dataN; j++) {
      plan[i].h_Data[j] = (float)rand() / (float)RAND_MAX;
    }
  }

  // Start timing and compute on GPU(s)
  printf("Computing with %d GPUs...\n", GPU_N);
  // create and start timer
  StopWatchInterface *timer = NULL;
  sdkCreateTimer(&timer);

  // start the timer
  sdkStartTimer(&timer);

  // Copy data to GPU, launch the kernel and copy data back. All asynchronously
  for (i = 0; i < GPU_N; i++) {
    // Set device
    checkCudaErrors(cudaSetDevice(i));

    // Copy input data from CPU
    checkCudaErrors(cudaMemcpyAsync(plan[i].d_Data, plan[i].h_Data,
                                    plan[i].dataN * sizeof(float),
                                    cudaMemcpyHostToDevice, plan[i].stream));

    // Perform GPU computations
    reduceKernel<<<BLOCK_N, THREAD_N, 0, plan[i].stream>>>(
        plan[i].d_Sum, plan[i].d_Data, plan[i].dataN);
    getLastCudaError("reduceKernel() execution failed.\n");

    // Read back GPU results
    checkCudaErrors(cudaMemcpyAsync(plan[i].h_Sum_from_device, plan[i].d_Sum,
                                    ACCUM_N * sizeof(float),
                                    cudaMemcpyDeviceToHost, plan[i].stream));
  }

  // Process GPU results
  for (i = 0; i < GPU_N; i++) {
    float sum;

    // Set device
    checkCudaErrors(cudaSetDevice(i));

    // Wait for all operations to finish
    cudaStreamSynchronize(plan[i].stream);

    // Finalize GPU reduction for current subvector
    sum = 0;

    for (j = 0; j < ACCUM_N; j++) {
      sum += plan[i].h_Sum_from_device[j];
    }

    *(plan[i].h_Sum) = (float)sum;

    // Shut down this GPU
    checkCudaErrors(cudaFreeHost(plan[i].h_Sum_from_device));
    checkCudaErrors(cudaFree(plan[i].d_Sum));
    checkCudaErrors(cudaFree(plan[i].d_Data));
    checkCudaErrors(cudaStreamDestroy(plan[i].stream));
  }

  sumGPU = 0;

  for (i = 0; i < GPU_N; i++) {
    sumGPU += h_SumGPU[i];
  }

  sdkStopTimer(&timer);
  printf("  GPU Processing time: %f (ms)\n\n", sdkGetTimerValue(&timer));
  sdkDeleteTimer(&timer);

  // Compute on Host CPU
  printf("Computing with Host CPU...\n\n");

  sumCPU = 0;

  for (i = 0; i < GPU_N; i++) {
    for (j = 0; j < plan[i].dataN; j++) {
      sumCPU += plan[i].h_Data[j];
    }
  }

  // Compare GPU and CPU results
  printf("Comparing GPU and Host CPU results...\n");
  diff = fabs(sumCPU - sumGPU) / fabs(sumCPU);
  printf("  GPU sum: %f\n  CPU sum: %f\n", sumGPU, sumCPU);
  printf("  Relative difference: %E \n\n", diff);

  // Cleanup and shutdown
  for (i = 0; i < GPU_N; i++) {
    checkCudaErrors(cudaSetDevice(i));
    checkCudaErrors(cudaFreeHost(plan[i].h_Data));
  }

  exit((diff < 1e-5) ? EXIT_SUCCESS : EXIT_FAILURE);
}