cuda-samples/Samples/5_Domain_Specific/MonteCarloMultiGPU/MonteCarlo_kernel.cu

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////////////////////////////////////////////////////////////////////////////////
// Global types
////////////////////////////////////////////////////////////////////////////////
#include <stdlib.h>
#include <stdio.h>
#include <cooperative_groups.h>

namespace cg = cooperative_groups;
#include <helper_cuda.h>
#include <curand_kernel.h>
#include "MonteCarlo_common.h"

////////////////////////////////////////////////////////////////////////////////
// Helper reduction template
// Please see the "reduction" CUDA Sample for more information
////////////////////////////////////////////////////////////////////////////////
#include "MonteCarlo_reduction.cuh"

////////////////////////////////////////////////////////////////////////////////
// Internal GPU-side data structures
////////////////////////////////////////////////////////////////////////////////
#define MAX_OPTIONS (1024 * 1024)

// Preprocessed input option data
typedef struct {
  real S;
  real X;
  real MuByT;
  real VBySqrtT;
} __TOptionData;

////////////////////////////////////////////////////////////////////////////////
// Overloaded shortcut payoff functions for different precision modes
////////////////////////////////////////////////////////////////////////////////
__device__ inline float endCallValue(float S, float X, float r, float MuByT,
                                     float VBySqrtT) {
  float callValue = S * __expf(MuByT + VBySqrtT * r) - X;
  return (callValue > 0.0F) ? callValue : 0.0F;
}

__device__ inline double endCallValue(double S, double X, double r,
                                      double MuByT, double VBySqrtT) {
  double callValue = S * exp(MuByT + VBySqrtT * r) - X;
  return (callValue > 0.0) ? callValue : 0.0;
}

#define THREAD_N 256

////////////////////////////////////////////////////////////////////////////////
// This kernel computes the integral over all paths using a single thread block
// per option. It is fastest when the number of thread blocks times the work per
// block is high enough to keep the GPU busy.
////////////////////////////////////////////////////////////////////////////////
static __global__ void MonteCarloOneBlockPerOption(
    curandState *__restrict rngStates,
    const __TOptionData *__restrict d_OptionData,
    __TOptionValue *__restrict d_CallValue, int pathN, int optionN) {
  // Handle to thread block group
  cg::thread_block cta = cg::this_thread_block();
  cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);

  const int SUM_N = THREAD_N;
  __shared__ real s_SumCall[SUM_N];
  __shared__ real s_Sum2Call[SUM_N];

  // determine global thread id
  int tid = threadIdx.x + blockIdx.x * blockDim.x;

  // Copy random number state to local memory for efficiency
  curandState localState = rngStates[tid];
  for (int optionIndex = blockIdx.x; optionIndex < optionN;
       optionIndex += gridDim.x) {
    const real S = d_OptionData[optionIndex].S;
    const real X = d_OptionData[optionIndex].X;
    const real MuByT = d_OptionData[optionIndex].MuByT;
    const real VBySqrtT = d_OptionData[optionIndex].VBySqrtT;

    // Cycle through the entire samples array:
    // derive end stock price for each path
    // accumulate partial integrals into intermediate shared memory buffer
    for (int iSum = threadIdx.x; iSum < SUM_N; iSum += blockDim.x) {
      __TOptionValue sumCall = {0, 0};

#pragma unroll 8
      for (int i = iSum; i < pathN; i += SUM_N) {
        real r = curand_normal(&localState);
        real callValue = endCallValue(S, X, r, MuByT, VBySqrtT);
        sumCall.Expected += callValue;
        sumCall.Confidence += callValue * callValue;
      }

      s_SumCall[iSum] = sumCall.Expected;
      s_Sum2Call[iSum] = sumCall.Confidence;
    }

    // Reduce shared memory accumulators
    // and write final result to global memory
    cg::sync(cta);
    sumReduce<real, SUM_N, THREAD_N>(s_SumCall, s_Sum2Call, cta, tile32,
                                     &d_CallValue[optionIndex]);
  }
}

static __global__ void rngSetupStates(curandState *rngState, int device_id) {
  // determine global thread id
  int tid = threadIdx.x + blockIdx.x * blockDim.x;
  // Each threadblock gets different seed,
  // Threads within a threadblock get different sequence numbers
  curand_init(blockIdx.x + gridDim.x * device_id, threadIdx.x, 0,
              &rngState[tid]);
}

////////////////////////////////////////////////////////////////////////////////
// Host-side interface to GPU Monte Carlo
////////////////////////////////////////////////////////////////////////////////

extern "C" void initMonteCarloGPU(TOptionPlan *plan) {
  checkCudaErrors(cudaMalloc(&plan->d_OptionData,
                             sizeof(__TOptionData) * (plan->optionCount)));
  checkCudaErrors(cudaMalloc(&plan->d_CallValue,
                             sizeof(__TOptionValue) * (plan->optionCount)));
  checkCudaErrors(cudaMallocHost(&plan->h_OptionData,
                                 sizeof(__TOptionData) * (plan->optionCount)));
  // Allocate internal device memory
  checkCudaErrors(cudaMallocHost(&plan->h_CallValue,
                                 sizeof(__TOptionValue) * (plan->optionCount)));
  // Allocate states for pseudo random number generators
  checkCudaErrors(cudaMalloc((void **)&plan->rngStates,
                             plan->gridSize * THREAD_N * sizeof(curandState)));
  checkCudaErrors(cudaMemset(plan->rngStates, 0,
                             plan->gridSize * THREAD_N * sizeof(curandState)));

  // place each device pathN random numbers apart on the random number sequence
  rngSetupStates<<<plan->gridSize, THREAD_N>>>(plan->rngStates, plan->device);
  getLastCudaError("rngSetupStates kernel failed.\n");
}

// Compute statistics and deallocate internal device memory
extern "C" void closeMonteCarloGPU(TOptionPlan *plan) {
  for (int i = 0; i < plan->optionCount; i++) {
    const double RT = plan->optionData[i].R * plan->optionData[i].T;
    const double sum = plan->h_CallValue[i].Expected;
    const double sum2 = plan->h_CallValue[i].Confidence;
    const double pathN = plan->pathN;
    // Derive average from the total sum and discount by riskfree rate
    plan->callValue[i].Expected = (float)(exp(-RT) * sum / pathN);
    // Standard deviation
    double stdDev = sqrt((pathN * sum2 - sum * sum) / (pathN * (pathN - 1)));
    // Confidence width; in 95% of all cases theoretical value lies within these
    // borders
    plan->callValue[i].Confidence =
        (float)(exp(-RT) * 1.96 * stdDev / sqrt(pathN));
  }

  checkCudaErrors(cudaFree(plan->rngStates));
  checkCudaErrors(cudaFreeHost(plan->h_CallValue));
  checkCudaErrors(cudaFreeHost(plan->h_OptionData));
  checkCudaErrors(cudaFree(plan->d_CallValue));
  checkCudaErrors(cudaFree(plan->d_OptionData));
}

// Main computations
extern "C" void MonteCarloGPU(TOptionPlan *plan, cudaStream_t stream) {
  __TOptionValue *h_CallValue = plan->h_CallValue;

  if (plan->optionCount <= 0 || plan->optionCount > MAX_OPTIONS) {
    printf("MonteCarloGPU(): bad option count.\n");
    return;
  }

  __TOptionData *h_OptionData = (__TOptionData *)plan->h_OptionData;

  for (int i = 0; i < plan->optionCount; i++) {
    const double T = plan->optionData[i].T;
    const double R = plan->optionData[i].R;
    const double V = plan->optionData[i].V;
    const double MuByT = (R - 0.5 * V * V) * T;
    const double VBySqrtT = V * sqrt(T);
    h_OptionData[i].S = (real)plan->optionData[i].S;
    h_OptionData[i].X = (real)plan->optionData[i].X;
    h_OptionData[i].MuByT = (real)MuByT;
    h_OptionData[i].VBySqrtT = (real)VBySqrtT;
  }

  checkCudaErrors(cudaMemcpyAsync(plan->d_OptionData, h_OptionData,
                                  plan->optionCount * sizeof(__TOptionData),
                                  cudaMemcpyHostToDevice, stream));

  MonteCarloOneBlockPerOption<<<plan->gridSize, THREAD_N, 0, stream>>>(
      plan->rngStates, (__TOptionData *)(plan->d_OptionData),
      (__TOptionValue *)(plan->d_CallValue), plan->pathN, plan->optionCount);
  getLastCudaError("MonteCarloOneBlockPerOption() execution failed\n");

  checkCudaErrors(cudaMemcpyAsync(h_CallValue, plan->d_CallValue,
                                  plan->optionCount * sizeof(__TOptionValue),
                                  cudaMemcpyDeviceToHost, stream));

  // cudaDeviceSynchronize();
}