Half a million Monte Carlo simulations per second! Release the power of your GPU from MS Excel 2010. Implementing user defined functions using CUDA ot OpenCL opens a whole new dimension in supercomputing in Microsoft Office.
The algorithm is composed of three parts:
1. Generate random numbers for n series
2. Compute the performance for each of these series using
3. Distribute performances into the resulting set.
Step 1: This is done using the curand nVidia librairy.
Step 2: This is performed by the performanceStorageKernel function described bellow.
Step 3: The trick here is to use an atomic operation (atomicAdd) to generate the resulting Gauss-like curve. Note that atomic operations are only availble on architectures equals or higher to sm_13. This is performed by the frequenciesKernel function.
Cuda Kernel
#include <curand.h>
#include <cuda.h>
#include <math.h>
#include <windows.h>
cudaDeviceProp _dDeviceProp;
// Device buffers
float* _dObjects;
float* _dSeries;
float*
_dPerformances;
int* _dFrequencies;
// Variables
int _dNbObjects;
int _dNbFrequencies;
int _dNbSeries;
/**
* @brief Initializes the testcase by
* - Allocating the GPU memory
* - Copying the buffer from RAM to GPU memory.
* This function is executed on the Host
* @ param hBuffer Pointer to array of floats in host
memory
* @ param N Number of objects in the array (an Objects
consists of 6 floats)
*/
extern "C"
void initialize_device( int
NbSeries, int NbObjects, int NbFrequencies )
{
// Testcase initialization
_dNbObjects = NbObjects;
_dNbFrequencies = NbFrequencies;
_dNbSeries = NbSeries;
// Device allocation
cudaMalloc( (void**)&_dObjects,
NbSeries*NbObjects*sizeof(float) );
cudaMalloc( (void**)&_dSeries,
NbSeries*sizeof(float)
);
cudaMalloc( (void**)&_dFrequencies,
NbFrequencies*sizeof(int)
);
cudaMalloc( (void**)&_dPerformances,
NbObjects*sizeof(float)
);
}
/**
* @brief
*/
__global__ void
performanceStorageKernel( float* series, float* objects, int
nbObjects, float* performances )
{
// Compute the index
unsigned int
x = blockIdx.x*blockDim.x+threadIdx.x;
unsigned int
y = blockIdx.y*blockDim.y+threadIdx.y;
unsigned int
index = (y*blockDim.x) + x;
int objectsIndex =
index*nbObjects;
// Compute performance
__shared__ float
localPerformance[2];
localPerformance[0] = 1.0; // current
performance
localPerformance[1] = 0.0; // previous
performance
for( int
i(0); i<nbObjects; ++i ) {
localPerformance[1] = localPerformance[0];
localPerformance[0] =
(1.0+objects[objectsIndex+i])*localPerformance[1];
if( index == 0 ) performances[i]
= localPerformance[0];
}
// Store performance
series[index] = localPerformance[0] - 1.0;
}
/**
* @brief Kernel function to be executed on the GPU
* @param ptr Pointer to an array of floats stored in GPU
memory
*/
__global__ void frequenciesKernel( float* series, float
range, int* frequencies, int nbFrequencies )
{
// Compute the index
unsigned int
x = blockIdx.x*blockDim.x+threadIdx.x;
unsigned int
y = blockIdx.y*blockDim.y+threadIdx.y;
unsigned int
index = (y*blockDim.x) + x;
float v =
series[index]-(-range/2);
int position =
(v/(range/nbFrequencies));
atomicAdd(&frequencies[position],1);
}
/**
* @brief Run the kernel on the GPU.
* This function is executed on the Host.
*/
extern "C"
void run_kernel( float range,
float mu, float
sigma, int random, int
nbThreadsPerBlock )
{
// Create pseudo-random number generator
curandGenerator_t gen;
curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_DEFAULT);
curandSetPseudoRandomGeneratorSeed(gen, random);
curandGenerateNormal(gen, _dObjects, _dNbSeries*_dNbObjects, mu,
sigma );
curandDestroyGenerator(gen);
dim3 block(8, 8, 1);
int gridDim = (int)sqrt(float(_dNbSeries));
dim3 gridPerformances( gridDim/block.x, gridDim/block.y, 1);
performanceStorageKernel<<<gridPerformances,block>>>(_dSeries,
_dObjects, _dNbObjects, _dPerformances );
// Reset memory
cudaMemset( _dFrequencies, 0, _dNbFrequencies*sizeof(int) );
// compute Frequencies
gridDim = (int)sqrt(float(_dNbSeries));
dim3 gridFrequencies( gridDim/block.x, gridDim/block.y, 1);
frequenciesKernel<<<gridFrequencies,block>>>(
_dSeries, range, _dFrequencies, _dNbFrequencies );
}
/*
* This function is executed on the Host.
* @brief Copy the data back to the Host and releases the
buffer on
* GPU device.
* This function is executed on the Host
* @ param hBuffer Pointer to array of floats in host
memory
* @ param N Number of objects in the array (an Objects
consists of 6 floats)
*/
extern "C"
void device_to_host( int*
hOutFrequencies, float* hOutPerformances, float* hOutSeries )
{
cudaMemcpy( hOutFrequencies, _dFrequencies, _dNbFrequencies*sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy( hOutPerformances, _dPerformances, _dNbObjects*sizeof(float),
cudaMemcpyDeviceToHost);
}
/*
* This function is executed on the Host.
* @brief Releases the buffer on GPU device.
* This function is executed on the Host
*/
extern "C"
void destroy_device()
{
cudaFree( _dObjects );
cudaFree( _dFrequencies );
cudaFree( _dSeries );
cudaFree(
_dPerformances );
}
