/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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 * 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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/*
 * See: https://www.piday.org/million/
 */

#include "MonteCarloPi.h"
#include <algorithm>
#define CUDA_DRIVER_API
#include <helper_cuda.h>
#include <iostream>

#define ROUND_UP_TO_GRANULARITY(x, n) (((x + n - 1) / n) * n)

// `ipcHandleTypeFlag` specifies the platform specific handle type this sample
// uses for importing and exporting memory allocation. On Linux this sample
// specifies the type as CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR meaning that
// file descriptors will be used. On Windows this sample specifies the type as
// CU_MEM_HANDLE_TYPE_WIN32 meaning that NT HANDLEs will be used. The
// ipcHandleTypeFlag variable is a convenience variable and is passed by value
// to individual requests.
#if defined(__linux__)
CUmemAllocationHandleType ipcHandleTypeFlag =
    CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR;
#else
CUmemAllocationHandleType ipcHandleTypeFlag = CU_MEM_HANDLE_TYPE_WIN32;
#endif

// Windows-specific LPSECURITYATTRIBUTES
void getDefaultSecurityDescriptor(CUmemAllocationProp *prop) {
#if defined(__linux__)
  return;
#elif defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
  static const char sddl[] = "D:P(OA;;GARCSDWDWOCCDCLCSWLODTWPRPCRFA;;;WD)";
  static OBJECT_ATTRIBUTES objAttributes;
  static bool objAttributesConfigured = false;

  if (!objAttributesConfigured) {
    PSECURITY_DESCRIPTOR secDesc;
    BOOL result = ConvertStringSecurityDescriptorToSecurityDescriptorA(
        sddl, SDDL_REVISION_1, &secDesc, NULL);
    if (result == 0) {
      printf("IPC failure: getDefaultSecurityDescriptor Failed! (%d)\n",
             GetLastError());
    }

    InitializeObjectAttributes(&objAttributes, NULL, 0, NULL, secDesc);

    objAttributesConfigured = true;
  }

  prop->win32HandleMetaData = &objAttributes;
  return;
#endif
}

__global__ void monte_carlo_kernel(vec2 *xyVector, float *pointsInsideCircle,
                                   float *numPointsInCircle,
                                   unsigned int numPoints, float time) {
  const size_t stride = gridDim.x * blockDim.x;
  size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
  float count = 0.0f;

  curandState rgnState;
  curand_init((unsigned long long)time, tid, 0, &rgnState);

  for (; tid < numPoints; tid += stride) {
    float x = curand_uniform(&rgnState);
    float y = curand_uniform(&rgnState);
    x = (2.0f * x) - 1.0f;
    y = (2.0f * y) - 1.0f;
    xyVector[tid][0] = x;
    xyVector[tid][1] = y;

    // Compute the distance of this point form the center(0, 0)
    float dist = sqrtf((x * x) + (y * y));

    // If distance is less than the radius of the unit circle, the point lies in
    // the circle.
    pointsInsideCircle[tid] = (dist <= 1.0f);
    count += (dist <= 1.0f);
  }
  atomicAdd(numPointsInCircle, count);
}

MonteCarloPiSimulation::MonteCarloPiSimulation(size_t num_points)
    : m_xyVector(nullptr),
      m_pointsInsideCircle(nullptr),
      m_totalPointsInsideCircle(0),
      m_totalPointsSimulated(0),
      m_numPoints(num_points) {}

MonteCarloPiSimulation::~MonteCarloPiSimulation() {
  if (m_numPointsInCircle) {
    checkCudaErrors(cudaFree(m_numPointsInCircle));
    m_numPointsInCircle = nullptr;
  }
  if (m_hostNumPointsInCircle) {
    checkCudaErrors(cudaFreeHost(m_hostNumPointsInCircle));
    m_hostNumPointsInCircle = nullptr;
  }

  cleanupSimulationAllocations();
}

void MonteCarloPiSimulation::initSimulation(int cudaDevice,
                                            cudaStream_t stream) {
  m_cudaDevice = cudaDevice;
  getIdealExecutionConfiguration();

  // Allocate a position buffer that contains random location of the points in
  // XY cartesian plane.
  // Allocate a bitmap buffer which holds information of whether a point in the
  // position buffer is inside the unit circle or not.
  setupSimulationAllocations();

  checkCudaErrors(
      cudaMalloc((float **)&m_numPointsInCircle, sizeof(*m_numPointsInCircle)));
  checkCudaErrors(cudaMallocHost((float **)&m_hostNumPointsInCircle,
                                 sizeof(*m_hostNumPointsInCircle)));
}

void MonteCarloPiSimulation::stepSimulation(float time, cudaStream_t stream) {
  checkCudaErrors(cudaMemsetAsync(m_numPointsInCircle, 0,
                                  sizeof(*m_numPointsInCircle), stream));

  monte_carlo_kernel<<<m_blocks, m_threads, 0, stream>>>(
      m_xyVector, m_pointsInsideCircle, m_numPointsInCircle, m_numPoints, time);
  getLastCudaError("Failed to launch CUDA simulation");

  checkCudaErrors(cudaMemcpyAsync(m_hostNumPointsInCircle, m_numPointsInCircle,
                                  sizeof(*m_numPointsInCircle),
                                  cudaMemcpyDeviceToHost, stream));

  // Queue up a stream callback to compute and print the PI value.
  checkCudaErrors(
      cudaLaunchHostFunc(stream, this->computePiCallback, (void *)this));
}

void MonteCarloPiSimulation::computePiCallback(void *args) {
  MonteCarloPiSimulation *cbData = (MonteCarloPiSimulation *)args;
  cbData->m_totalPointsInsideCircle += *(cbData->m_hostNumPointsInCircle);
  cbData->m_totalPointsSimulated += cbData->m_numPoints;
  double piValue = 4.0 * ((double)cbData->m_totalPointsInsideCircle /
                          (double)cbData->m_totalPointsSimulated);
  printf("Approximate Pi value for %zd data points: %lf \n",
         cbData->m_totalPointsSimulated, piValue);
}

void MonteCarloPiSimulation::getIdealExecutionConfiguration() {
  int warpSize = 0;
  int multiProcessorCount = 0;

  checkCudaErrors(cudaSetDevice(m_cudaDevice));
  checkCudaErrors(
      cudaDeviceGetAttribute(&warpSize, cudaDevAttrWarpSize, m_cudaDevice));

  // We don't need large block sizes, since there's not much inter-thread
  // communication
  m_threads = warpSize;

  // Use the occupancy calculator and fill the gpu as best as we can
  checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
      &m_blocks, monte_carlo_kernel, warpSize, 0));

  checkCudaErrors(cudaDeviceGetAttribute(
      &multiProcessorCount, cudaDevAttrMultiProcessorCount, m_cudaDevice));
  m_blocks *= multiProcessorCount;

  // Go ahead and the clamp the blocks to the minimum needed for this
  // height/width
  m_blocks =
      std::min(m_blocks, (int)((m_numPoints + m_threads - 1) / m_threads));
}

void MonteCarloPiSimulation::setupSimulationAllocations() {
  CUdeviceptr d_ptr = 0U;
  size_t granularity = 0;
  CUmemGenericAllocationHandle cudaPositionHandle, cudaInCircleHandle;

  CUmemAllocationProp allocProp = {};
  allocProp.type = CU_MEM_ALLOCATION_TYPE_PINNED;
  allocProp.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
  allocProp.location.id = m_cudaDevice;
  allocProp.win32HandleMetaData = NULL;
  allocProp.requestedHandleTypes = ipcHandleTypeFlag;

  // Windows-specific LPSECURITYATTRIBUTES is required when
  // CU_MEM_HANDLE_TYPE_WIN32 is used. The security attribute defines the scope
  // of which exported allocations may be tranferred to other processes. For all
  // other handle types, pass NULL.
  getDefaultSecurityDescriptor(&allocProp);

  // Get the recommended granularity for m_cudaDevice.
  checkCudaErrors(cuMemGetAllocationGranularity(
      &granularity, &allocProp, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));

  size_t xyPositionVecSize = m_numPoints * sizeof(*m_xyVector);
  size_t inCircleVecSize = m_numPoints * sizeof(*m_pointsInsideCircle);

  size_t xyPositionSize =
      ROUND_UP_TO_GRANULARITY(xyPositionVecSize, granularity);
  size_t inCircleSize = ROUND_UP_TO_GRANULARITY(inCircleVecSize, granularity);
  m_totalAllocationSize = (xyPositionSize + inCircleSize);

  // Reserve the required contiguous VA space for the allocations
  checkCudaErrors(
      cuMemAddressReserve(&d_ptr, m_totalAllocationSize, granularity, 0U, 0));

  // Create the allocations as a pinned allocation on this device.
  // Create an allocation to store all the positions of points on the xy plane
  // and a second allocation which stores information if the corresponding
  // position is inside the unit circle or not.
  checkCudaErrors(
      cuMemCreate(&cudaPositionHandle, xyPositionSize, &allocProp, 0));
  checkCudaErrors(
      cuMemCreate(&cudaInCircleHandle, inCircleSize, &allocProp, 0));

  // Export the allocation to a platform-specific handle. The type of handle
  // requested here must match the requestedHandleTypes field in the prop
  // structure passed to cuMemCreate. The handle obtained here will be passed to
  // vulkan to import the allocation.
  checkCudaErrors(cuMemExportToShareableHandle(
      (void *)&m_posShareableHandle, cudaPositionHandle, ipcHandleTypeFlag, 0));
  checkCudaErrors(
      cuMemExportToShareableHandle((void *)&m_inCircleShareableHandle,
                                   cudaInCircleHandle, ipcHandleTypeFlag, 0));

  CUdeviceptr va_position = d_ptr;
  CUdeviceptr va_InCircle = va_position + xyPositionSize;
  m_pointsInsideCircle = (float *)va_InCircle;
  m_xyVector = (vec2 *)va_position;

  // Assign the chunk to the appropriate VA range
  checkCudaErrors(
      cuMemMap(va_position, xyPositionSize, 0, cudaPositionHandle, 0));
  checkCudaErrors(
      cuMemMap(va_InCircle, inCircleSize, 0, cudaInCircleHandle, 0));

  // Release the handles for the allocation. Since the allocation is currently
  // mapped to a VA range with a previous call to cuMemMap the actual freeing of
  // memory allocation will happen on an eventual call to cuMemUnmap. Thus the
  // allocation will be kept live until it is unmapped.
  checkCudaErrors(cuMemRelease(cudaPositionHandle));
  checkCudaErrors(cuMemRelease(cudaInCircleHandle));

  CUmemAccessDesc accessDescriptor = {};
  accessDescriptor.location.id = m_cudaDevice;
  accessDescriptor.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
  accessDescriptor.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;

  // Apply the access descriptor to the whole VA range. Essentially enables
  // Read-Write access to the range.
  checkCudaErrors(
      cuMemSetAccess(d_ptr, m_totalAllocationSize, &accessDescriptor, 1));
}

void MonteCarloPiSimulation::cleanupSimulationAllocations() {
  if (m_xyVector && m_pointsInsideCircle) {
    // Unmap the mapped virtual memory region
    // Since the handles to the mapped backing stores have already been released
    // by cuMemRelease, and these are the only/last mappings referencing them,
    // The backing stores will be freed.
    checkCudaErrors(cuMemUnmap((CUdeviceptr)m_xyVector, m_totalAllocationSize));

    checkIpcErrors(ipcCloseShareableHandle(m_posShareableHandle));
    checkIpcErrors(ipcCloseShareableHandle(m_inCircleShareableHandle));

    // Free the virtual address region.
    checkCudaErrors(
        cuMemAddressFree((CUdeviceptr)m_xyVector, m_totalAllocationSize));

    m_xyVector = nullptr;
    m_pointsInsideCircle = nullptr;
  }
}