/* Copyright (c) 2020, 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,
 * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */

// Includes, system
#include <stdio.h>

// Includes CUDA
#include <cuda_runtime.h>
#include <cuda/barrier>
#include <cooperative_groups.h>

// Utilities and timing functions
#include <helper_functions.h>  // includes cuda.h and cuda_runtime_api.h

// CUDA helper functions
#include <helper_cuda.h>  // helper functions for CUDA error check

namespace cg = cooperative_groups;

#if __CUDA_ARCH__ >= 700
template <bool writeSquareRoot>
__device__ void reduceBlockData(
    cuda::barrier<cuda::thread_scope_block> &barrier,
    cg::thread_block_tile<32> &tile32, double &threadSum, double *result) {
  extern __shared__ double tmp[];

#pragma unroll
  for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
    threadSum += tile32.shfl_down(threadSum, offset);
  }
  if (tile32.thread_rank() == 0) {
    tmp[tile32.meta_group_rank()] = threadSum;
  }

  auto token = barrier.arrive();

  barrier.wait(std::move(token));

  // The warp 0 will perform last round of reduction
  if (tile32.meta_group_rank() == 0) {
    double beta = tile32.thread_rank() < tile32.meta_group_size()
                      ? tmp[tile32.thread_rank()]
                      : 0.0;

#pragma unroll
    for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
      beta += tile32.shfl_down(beta, offset);
    }

    if (tile32.thread_rank() == 0) {
      if (writeSquareRoot)
        *result = sqrt(beta);
      else
        *result = beta;
    }
  }
}
#endif

__global__ void normVecByDotProductAWBarrier(float *vecA, float *vecB,
                                             double *partialResults, int size) {
#if __CUDA_ARCH__ >= 700
#pragma diag_suppress static_var_with_dynamic_init
  cg::thread_block cta = cg::this_thread_block();
  cg::grid_group grid = cg::this_grid();
  ;
  cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);

  __shared__ cuda::barrier<cuda::thread_scope_block> barrier;

  if (threadIdx.x == 0) {
    init(&barrier, blockDim.x);
  }

  cg::sync(cta);

  double threadSum = 0.0;
  for (int i = grid.thread_rank(); i < size; i += grid.size()) {
    threadSum += (double)(vecA[i] * vecB[i]);
  }

  // Each thread block performs reduction of partial dotProducts and writes to
  // global mem.
  reduceBlockData<false>(barrier, tile32, threadSum,
                         &partialResults[blockIdx.x]);

  cg::sync(grid);

  // One block performs the final summation of partial dot products
  // of all the thread blocks and writes the sqrt of final dot product.
  if (blockIdx.x == 0) {
    threadSum = 0.0;
    for (int i = cta.thread_rank(); i < gridDim.x; i += cta.size()) {
      threadSum += partialResults[i];
    }
    reduceBlockData<true>(barrier, tile32, threadSum, &partialResults[0]);
  }

  cg::sync(grid);

  const double finalValue = partialResults[0];

  // Perform normalization of vecA & vecB.
  for (int i = grid.thread_rank(); i < size; i += grid.size()) {
    vecA[i] = (float)vecA[i] / finalValue;
    vecB[i] = (float)vecB[i] / finalValue;
  }
#endif
}

int runNormVecByDotProductAWBarrier(int argc, char **argv, int deviceId);

////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
  printf("%s starting...\n", argv[0]);

  // This will pick the best possible CUDA capable device
  int dev = findCudaDevice(argc, (const char **)argv);

  int major = 0;
  checkCudaErrors(
      cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, dev));

  // Arrive-Wait Barrier require a GPU of Volta (SM7X) architecture or higher.
  if (major < 7) {
    printf("simpleAWBarrier requires SM 7.0 or higher.  Exiting...\n");
    exit(EXIT_WAIVED);
  }

  int supportsCooperativeLaunch = 0;
  checkCudaErrors(cudaDeviceGetAttribute(&supportsCooperativeLaunch,
                                         cudaDevAttrCooperativeLaunch, dev));

  if (!supportsCooperativeLaunch) {
    printf(
        "\nSelected GPU (%d) does not support Cooperative Kernel Launch, "
        "Waiving the run\n",
        dev);
    exit(EXIT_WAIVED);
  }

  int testResult = runNormVecByDotProductAWBarrier(argc, argv, dev);

  printf("%s completed, returned %s\n", argv[0], testResult ? "OK" : "ERROR!");
  exit(testResult ? EXIT_SUCCESS : EXIT_FAILURE);
}

int runNormVecByDotProductAWBarrier(int argc, char **argv, int deviceId) {
  float *vecA, *d_vecA;
  float *vecB, *d_vecB;
  double *d_partialResults;
  int size = 10000000;

  checkCudaErrors(cudaMallocHost(&vecA, sizeof(float) * size));
  checkCudaErrors(cudaMallocHost(&vecB, sizeof(float) * size));

  checkCudaErrors(cudaMalloc(&d_vecA, sizeof(float) * size));
  checkCudaErrors(cudaMalloc(&d_vecB, sizeof(float) * size));

  float baseVal = 2.0;
  for (int i = 0; i < size; i++) {
    vecA[i] = vecB[i] = baseVal;
  }

  cudaStream_t stream;
  checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));

  checkCudaErrors(cudaMemcpyAsync(d_vecA, vecA, sizeof(float) * size,
                                  cudaMemcpyHostToDevice, stream));
  checkCudaErrors(cudaMemcpyAsync(d_vecB, vecB, sizeof(float) * size,
                                  cudaMemcpyHostToDevice, stream));

  // Kernel configuration, where a one-dimensional
  // grid and one-dimensional blocks are configured.
  int minGridSize = 0, blockSize = 0;
  checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(
      &minGridSize, &blockSize, (void *)normVecByDotProductAWBarrier, 0, size));

  int smemSize = ((blockSize / 32) + 1) * sizeof(double);

  int numBlocksPerSm = 0;
  checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
      &numBlocksPerSm, normVecByDotProductAWBarrier, blockSize, smemSize));

  int multiProcessorCount = 0;
  checkCudaErrors(cudaDeviceGetAttribute(
      &multiProcessorCount, cudaDevAttrMultiProcessorCount, deviceId));

  minGridSize = multiProcessorCount * numBlocksPerSm;
  checkCudaErrors(cudaMalloc(&d_partialResults, minGridSize * sizeof(double)));

  printf(
      "Launching normVecByDotProductAWBarrier kernel with numBlocks = %d "
      "blockSize = %d\n",
      minGridSize, blockSize);

  dim3 dimGrid(minGridSize, 1, 1), dimBlock(blockSize, 1, 1);

  void *kernelArgs[] = {(void *)&d_vecA, (void *)&d_vecB,
                        (void *)&d_partialResults, (void *)&size};

  checkCudaErrors(
      cudaLaunchCooperativeKernel((void *)normVecByDotProductAWBarrier, dimGrid,
                                  dimBlock, kernelArgs, smemSize, stream));

  checkCudaErrors(cudaMemcpyAsync(vecA, d_vecA, sizeof(float) * size,
                                  cudaMemcpyDeviceToHost, stream));
  checkCudaErrors(cudaStreamSynchronize(stream));

  float expectedResult = (baseVal / sqrt(size * baseVal * baseVal));
  unsigned int matches = 0;
  for (int i = 0; i < size; i++) {
    if ((vecA[i] - expectedResult) > 0.00001) {
      printf("mismatch at i = %d\n", i);
      break;
    } else {
      matches++;
    }
  }

  printf("Result = %s\n", matches == size ? "PASSED" : "FAILED");
  checkCudaErrors(cudaFree(d_vecA));
  checkCudaErrors(cudaFree(d_vecB));
  checkCudaErrors(cudaFree(d_partialResults));

  checkCudaErrors(cudaFreeHost(vecA));
  checkCudaErrors(cudaFreeHost(vecB));
  return matches == size;
}
