cuda-samples/Samples/5_Domain_Specific/SobolQRNG/sobol_gpu.cu

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/*
 * Portions Copyright (c) 2009 Mike Giles, Oxford University.  All rights
 * reserved.
 * Portions Copyright (c) 2008 Frances Y. Kuo and Stephen Joe.  All rights
 * reserved.
 *
 * Sobol Quasi-random Number Generator example
 *
 * Based on CUDA code submitted by Mike Giles, Oxford University, United Kingdom
 * http://people.maths.ox.ac.uk/~gilesm/
 *
 * and C code developed by Stephen Joe, University of Waikato, New Zealand
 * and Frances Kuo, University of New South Wales, Australia
 * http://web.maths.unsw.edu.au/~fkuo/sobol/
 *
 * For theoretical background see:
 *
 * P. Bratley and B.L. Fox.
 * Implementing Sobol's quasirandom sequence generator
 * http://portal.acm.org/citation.cfm?id=42288
 * ACM Trans. on Math. Software, 14(1):88-100, 1988
 *
 * S. Joe and F. Kuo.
 * Remark on algorithm 659: implementing Sobol's quasirandom sequence generator.
 * http://portal.acm.org/citation.cfm?id=641879
 * ACM Trans. on Math. Software, 29(1):49-57, 2003
 *
 */

#include "sobol.h"
#include "sobol_gpu.h"
#include <cooperative_groups.h>

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

#define k_2powneg32 2.3283064E-10F

__global__ void sobolGPU_kernel(unsigned n_vectors, unsigned n_dimensions,
                                unsigned *d_directions, float *d_output) {
  // Handle to thread block group
  cg::thread_block cta = cg::this_thread_block();
  __shared__ unsigned int v[n_directions];

  // Offset into the correct dimension as specified by the
  // block y coordinate
  d_directions = d_directions + n_directions * blockIdx.y;
  d_output = d_output + n_vectors * blockIdx.y;

  // Copy the direction numbers for this dimension into shared
  // memory - there are only 32 direction numbers so only the
  // first 32 (n_directions) threads need participate.
  if (threadIdx.x < n_directions) {
    v[threadIdx.x] = d_directions[threadIdx.x];
  }

  cg::sync(cta);

  // Set initial index (i.e. which vector this thread is
  // computing first) and stride (i.e. step to the next vector
  // for this thread)
  int i0 = threadIdx.x + blockIdx.x * blockDim.x;
  int stride = gridDim.x * blockDim.x;

  // Get the gray code of the index
  // c.f. Numerical Recipes in C, chapter 20
  // http://www.nrbook.com/a/bookcpdf/c20-2.pdf
  unsigned int g = i0 ^ (i0 >> 1);

  // Initialisation for first point x[i0]
  // In the Bratley and Fox paper this is equation (*), where
  // we are computing the value for x[n] without knowing the
  // value of x[n-1].
  unsigned int X = 0;
  unsigned int mask;

  for (unsigned int k = 0; k < __ffs(stride) - 1; k++) {
    // We want X ^= g_k * v[k], where g_k is one or zero.
    // We do this by setting a mask with all bits equal to
    // g_k. In reality we keep shifting g so that g_k is the
    // LSB of g. This way we avoid multiplication.
    mask = -(g & 1);
    X ^= mask & v[k];
    g = g >> 1;
  }

  if (i0 < n_vectors) {
    d_output[i0] = (float)X * k_2powneg32;
  }

  // Now do rest of points, using the stride
  // Here we want to generate x[i] from x[i-stride] where we
  // don't have any of the x in between, therefore we have to
  // revisit the equation (**), this is easiest with an example
  // so assume stride is 16.
  // From x[n] to x[n+16] there will be:
  //   8 changes in the first bit
  //   4 changes in the second bit
  //   2 changes in the third bit
  //   1 change in the fourth
  //   1 change in one of the remaining bits
  //
  // What this means is that in the equation:
  //   x[n+1] = x[n] ^ v[p]
  //   x[n+2] = x[n+1] ^ v[q] = x[n] ^ v[p] ^ v[q]
  //   ...
  // We will apply xor with v[1] eight times, v[2] four times,
  // v[3] twice, v[4] once and one other direction number once.
  // Since two xors cancel out, we can skip even applications
  // and just apply xor with v[4] (i.e. log2(16)) and with
  // the current applicable direction number.
  // Note that all these indices count from 1, so we need to
  // subtract 1 from them all to account for C arrays counting
  // from zero.
  unsigned int v_log2stridem1 = v[__ffs(stride) - 2];
  unsigned int v_stridemask = stride - 1;

  for (unsigned int i = i0 + stride; i < n_vectors; i += stride) {
    // x[i] = x[i-stride] ^ v[b] ^ v[c]
    //  where b is log2(stride) minus 1 for C array indexing
    //  where c is the index of the rightmost zero bit in i,
    //  not including the bottom log2(stride) bits, minus 1
    //  for C array indexing
    // In the Bratley and Fox paper this is equation (**)
    X ^= v_log2stridem1 ^ v[__ffs(~((i - stride) | v_stridemask)) - 1];
    d_output[i] = (float)X * k_2powneg32;
  }
}

extern "C" void sobolGPU(int n_vectors, int n_dimensions,
                         unsigned int *d_directions, float *d_output) {
  const int threadsperblock = 64;

  // Set up the execution configuration
  dim3 dimGrid;
  dim3 dimBlock;

  int device;
  cudaDeviceProp prop;
  checkCudaErrors(cudaGetDevice(&device));
  checkCudaErrors(cudaGetDeviceProperties(&prop, device));

  // This implementation of the generator outputs all the draws for
  // one dimension in a contiguous region of memory, followed by the
  // next dimension and so on.
  // Therefore all threads within a block will be processing different
  // vectors from the same dimension. As a result we want the total
  // number of blocks to be a multiple of the number of dimensions.
  dimGrid.y = n_dimensions;

  // If the number of dimensions is large then we will set the number
  // of blocks to equal the number of dimensions (i.e. dimGrid.x = 1)
  // but if the number of dimensions is small (e.g. less than four per
  // multiprocessor) then we'll partition the vectors across blocks
  // (as well as threads).
  if (n_dimensions < (4 * prop.multiProcessorCount)) {
    dimGrid.x = 4 * prop.multiProcessorCount;
  } else {
    dimGrid.x = 1;
  }

  // Cap the dimGrid.x if the number of vectors is small
  if (dimGrid.x > (unsigned int)(n_vectors / threadsperblock)) {
    dimGrid.x = (n_vectors + threadsperblock - 1) / threadsperblock;
  }

  // Round up to a power of two, required for the algorithm so that
  // stride is a power of two.
  unsigned int targetDimGridX = dimGrid.x;

  for (dimGrid.x = 1; dimGrid.x < targetDimGridX; dimGrid.x *= 2)
    ;

  // Fix the number of threads
  dimBlock.x = threadsperblock;

  // Execute GPU kernel
  sobolGPU_kernel<<<dimGrid, dimBlock>>>(n_vectors, n_dimensions, d_directions,
                                         d_output);
}