CUDA.tpl 26.9 KB
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\TEMPLATE_START
#ifdef WIN32
#define _CRT_SECURE_NO_WARNINGS
#pragma comment(lib, "libEasea.lib")
#pragma comment(lib, "Winmm.lib")
#endif
/**
 This is program entry for STD template for EASEA

*/

\ANALYSE_PARAMETERS
#include <stdlib.h>
#include <iostream>
#include <time.h>
#include "COptionParser.h"
#include "CRandomGenerator.h"
#include "CEvolutionaryAlgorithm.h"
#include "global.h"
#include "EASEAIndividual.hpp"

using namespace std;

/** Global variables for the whole algorithm */
CIndividual** pPopulation = NULL;
CIndividual* bBest = NULL;
float* pEZ_MUT_PROB = NULL;
float* pEZ_XOVER_PROB = NULL;
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unsigned *EZ_NB_GEN;
unsigned *EZ_current_generation;
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CEvolutionaryAlgorithm* EA;
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int main(int argc, char** argv){


	parseArguments("EASEA.prm",argc,argv);

	ParametersImpl p;
	p.setDefaultParameters(argc,argv);
	CEvolutionaryAlgorithm* ea = p.newEvolutionaryAlgorithm();

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	EA = ea;

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	EASEAInit(argc,argv);

	CPopulation* pop = ea->getPopulation();

	ea->runEvolutionaryLoop();

	EASEAFinal(pop);

	delete pop;

#ifdef WIN32
	system("pause");
#endif
	return 0;
}

\START_CUDA_GENOME_CU_TPL
#ifdef _WIN32
#define _CRT_SECURE_NO_WARNINGS
#define WIN32
#endif

#include <fstream>
#ifndef WIN32
#include <sys/time.h>
#else
#include <time.h>
#endif
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#include <string>
#include <sstream>
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#include "CRandomGenerator.h"
#include "CPopulation.h"
#include "COptionParser.h"
#include "CStoppingCriterion.h"
#include "CEvolutionaryAlgorithm.h"
#include "global.h"
#include "CIndividual.h"
#include "CCuda.h"
#include <vector_types.h>


using namespace std;

#include "EASEAIndividual.hpp"
bool INSTEAD_EVAL_STEP = false;

CRandomGenerator* globalRandomGenerator;
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extern CEvolutionaryAlgorithm *EA;
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#define CUDA_TPL

struct gpuArg* gpuArgs;


struct my_struct_gpu* gpu_infos;
float* fitnessTemp;  
bool freeGPU = false;
bool first_generation = true;
int num_gpus = 0;       // number of CUDA GPUs

PopulationImpl* Pop = NULL;

\INSERT_USER_DECLARATIONS
\ANALYSE_USER_CLASSES

\INSERT_USER_CLASSES

\INSERT_USER_FUNCTIONS

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void cudaPreliminaryProcess(unsigned PopulationSize){
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       int capacite_max = 0;

       //Recuperation of each device information's.
       for( int index = 0; index < num_gpus; index++){
             cudaDeviceProp deviceProp;
             cudaGetDeviceProperties(&deviceProp, index);

             gpu_infos[index].num_MP =  deviceProp.multiProcessorCount*2; //Two block on each MP
	     gpu_infos[index].num_thread_max = deviceProp.maxThreadsPerBlock*0.5; //We are going to use 50% of the real maximun thread per block, we want to be sure to have enough memory for all of them. 
             gpu_infos[index].num_Warp = deviceProp.warpSize;
             capacite_max += gpu_infos[index].num_MP * gpu_infos[index].num_thread_max;
       }
      
       int count = 0;

       //We can have different cards that's why we are going to put more or less individuals on each of them according to their respective capacity.
       for( int index = 0; index < num_gpus; index++){
           gpu_infos[index].indiv_start = count;
           //On the first cards we are going to place a maximun of individuals.
           if(index != (num_gpus - 1)) 
	    gpu_infos[index].sh_pop_size = ceil((float)PopulationSize * (((float)gpu_infos[index].num_MP*(float)gpu_infos[index].num_thread_max) / (float)capacite_max) );
           //On the last card we are going to place the remaining individuals.  
          else 
            gpu_infos[index].sh_pop_size = PopulationSize - count;
                    
          count += gpu_infos[index].sh_pop_size;
	  
          /*
	  * The number of thread will be a multiple of the number of Warp less than or equal at the maximun number of thread per block.
	  * The number of block will be a multiple of the double of MP.
	  */
          if( !repartition(&gpu_infos[index]))
                 exit( -1 );
           std::cout << "Device number : " << index << "  Number of block : " << gpu_infos[index].dimGrid << std::endl;
           std::cout << "Device number : " << index << "  Number of thread : " << gpu_infos[index].dimBlock << std::endl;
       }
}

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__device__ __host__ inline IndividualImpl* INDIVIDUAL_ACCESS(void* buffer,unsigned id){
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  return (IndividualImpl*)buffer+id;
}

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__device__ float cudaEvaluate(void* devBuffer, unsigned id, struct gpuOptions initOpts){
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  \INSERT_CUDA_EVALUATOR
}
  

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__global__ void cudaEvaluatePopulation(void* d_population, unsigned popSize, float* d_fitnesses, struct gpuOptions initOpts){
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        unsigned id = (blockDim.x*blockIdx.x)+threadIdx.x;  // id of the individual computed by this thread
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  	// escaping for the last block
        if( id >= popSize ) return;
  
        //void* indiv = ((char*)d_population)+id*(\GENOME_SIZE+sizeof(IndividualImpl*)); // compute the offset of the current individual
        d_fitnesses[id] = cudaEvaluate(d_population,id,initOpts);
}



void* gpuThreadMain(void* arg){

  cudaError_t lastError;
  struct gpuArg* localArg = (struct gpuArg*)arg;
  cudaSetDevice(localArg->threadId);
  int nbr_cudaPreliminaryProcess = 2;

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  // Because of the context of each GPU thread, we have to put all user's CUDA 
  // initialisation here if we want to use them in the GPU, otherwise they are
  // not found in the GPU context
  \INSERT_USER_CUDA

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  // Wait for population to evaluate
   while(1){
	    sem_wait(&localArg->sem_in);
	    if( freeGPU ) {
                        cudaFree(localArg->d_fitness);
	                cudaFree(localArg->d_population);
     			 break;
	    }
	    if(nbr_cudaPreliminaryProcess > 0) {
              	 lastError = cudaMalloc(&localArg->d_population,gpu_infos[localArg->threadId].sh_pop_size*(sizeof(IndividualImpl)));
	         lastError = cudaMalloc(((void**)&localArg->d_fitness),gpu_infos[localArg->threadId].sh_pop_size*sizeof(float));
	         nbr_cudaPreliminaryProcess--;
            }		    
            lastError = cudaMemcpy(localArg->d_population,(IndividualImpl*)(Pop->cuda->cudaBuffer)+gpu_infos[localArg->threadId].indiv_start,(sizeof(IndividualImpl)*gpu_infos[localArg->threadId].sh_pop_size),cudaMemcpyHostToDevice);
				      
	    cudaEvaluatePopulation<<< gpu_infos[localArg->threadId].dimGrid, gpu_infos[localArg->threadId].dimBlock>>>(localArg->d_population,gpu_infos[localArg->threadId].sh_pop_size,localArg->d_fitness,Pop->cuda->initOpts);
	    lastError = cudaThreadSynchronize();
		    
	    lastError = cudaMemcpy(fitnessTemp + gpu_infos[localArg->threadId].indiv_start, localArg->d_fitness, gpu_infos[localArg->threadId].sh_pop_size*sizeof(float), cudaMemcpyDeviceToHost);
	    
	    sem_post(&localArg->sem_out);
   }
  sem_post(&localArg->sem_out);
  fflush(stdout);
  return NULL;
}
				
void wake_up_gpu_thread(){
	for( int i=0 ; i<num_gpus ; i++ ){
		sem_post(&(gpuArgs[i].sem_in));
		sem_wait(&gpuArgs[i].sem_out);
  	}
}
				
void InitialiseGPUs(){
	//MultiGPU part on one CPU
	gpuArgs = (struct gpuArg*)malloc(sizeof(struct gpuArg)*num_gpus);
	pthread_t* t = (pthread_t*)malloc(sizeof(pthread_t)*num_gpus);
	
	//here we want to create on thread per GPU
	for( int i=0 ; i<num_gpus ; i++ ){
	  	gpuArgs[i].threadId = i;
	  	sem_init(&gpuArgs[i].sem_in,0,0);
	  	sem_init(&gpuArgs[i].sem_out,0,0);
	  	if( pthread_create(t+i,NULL,gpuThreadMain,gpuArgs+i) )
		  	perror("pthread_create : ");
	}
}

\INSERT_INITIALISATION_FUNCTION
\INSERT_FINALIZATION_FUNCTION

\INSERT_BOUND_CHECKING

void evale_pop_chunk(CIndividual** population, int popSize){
  \INSTEAD_EVAL_FUNCTION
}

void EASEAInit(int argc, char** argv){
        cudaGetDeviceCount(&num_gpus);
        gpu_infos = (struct my_struct_gpu*)malloc(sizeof(struct my_struct_gpu)*num_gpus);
	InitialiseGPUs();
	\INSERT_INIT_FCT_CALL
}

void EASEAFinal(CPopulation* pop){
	freeGPU=true;
	wake_up_gpu_thread();
        free(gpuArgs);
	
        free(gpu_infos);
	\INSERT_FINALIZATION_FCT_CALL;
}

void AESAEBeginningGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm){
	\INSERT_BEGIN_GENERATION_FUNCTION
}

void AESAEEndGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm){
	\INSERT_END_GENERATION_FUNCTION
}

void AESAEGenerationFunctionBeforeReplacement(CEvolutionaryAlgorithm* evolutionaryAlgorithm){
        \INSERT_GENERATION_FUNCTION_BEFORE_REPLACEMENT
}


IndividualImpl::IndividualImpl() : CIndividual() {
  \GENOME_CTOR 
  \INSERT_EO_INITIALISER
  valid = false;
}

CIndividual* IndividualImpl::clone(){
	return new IndividualImpl(*this);
}

IndividualImpl::~IndividualImpl(){
  \GENOME_DTOR
}


float IndividualImpl::evaluate(){
  if(valid)
    return fitness;
  else{
    valid = true;
    \INSERT_EVALUATOR
  }
}

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string IndividualImpl::serialize(){
    ostringstream AESAE_Line(ios_base::app);
    \GENOME_SERIAL
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    AESAE_Line << this->fitness;
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    return AESAE_Line.str();
}

void IndividualImpl::deserialize(string Line){
    istringstream AESAE_Line(Line);
    string line;
    \GENOME_DESERIAL
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    AESAE_Line >> this->fitness;
    this->valid=true;
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}

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IndividualImpl::IndividualImpl(const IndividualImpl& genome){

  // ********************
  // Problem specific part
  \COPY_CTOR


  // ********************
  // Generic part
  this->valid = genome.valid;
  this->fitness = genome.fitness;
}


CIndividual* IndividualImpl::crossover(CIndividual** ps){
	// ********************
	// Generic part
	IndividualImpl** tmp = (IndividualImpl**)ps;
	IndividualImpl parent1(*this);
	IndividualImpl parent2(*tmp[0]);
	IndividualImpl child(*this);

	//DEBUG_PRT("Xover");
	/*   cout << "p1 : " << parent1 << endl; */
	/*   cout << "p2 : " << parent2 << endl; */

	// ********************
	// Problem specific part
  	\INSERT_CROSSOVER


	child.valid = false;
	/*   cout << "child : " << child << endl; */
	return new IndividualImpl(child);
}


void IndividualImpl::printOn(std::ostream& os) const{
	\INSERT_DISPLAY
}

std::ostream& operator << (std::ostream& O, const IndividualImpl& B)
{
  // ********************
  // Problem specific part
  O << "\nIndividualImpl : "<< std::endl;
  O << "\t\t\t";
  B.printOn(O);

  if( B.valid ) O << "\t\t\tfitness : " << B.fitness;
  else O << "fitness is not yet computed" << std::endl;
  return O;
}


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unsigned IndividualImpl::mutate( float pMutationPerGene ){
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  this->valid=false;


  // ********************
  // Problem specific part
  \INSERT_MUTATOR
}


void PopulationImpl::evaluateParentPopulation(){
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        unsigned actualPopulationSize = this->actualParentPopulationSize;
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	fitnessTemp = new float[actualPopulationSize];
	int index;
        cudaPreliminaryProcess(actualPopulationSize);
        
	       	
 	wake_up_gpu_thread(); 

 	
	for( index=(actualPopulationSize-1); index>=0; index--){
		this->parents[index]->fitness = fitnessTemp[index];
		this->parents[index]->valid = true;
	}  

        delete[](fitnessTemp);

}

void PopulationImpl::evaluateOffspringPopulation(){
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	unsigned actualPopulationSize = this->actualOffspringPopulationSize;
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	fitnessTemp = new float[actualPopulationSize];
	int index;
	if(first_generation) cudaPreliminaryProcess(actualPopulationSize);

        for( index=(actualPopulationSize-1); index>=0; index--)
	    ((IndividualImpl*)this->offsprings[index])->copyToCudaBuffer(this->cuda->cudaBuffer,index);

        wake_up_gpu_thread(); 

	for( index=(actualPopulationSize-1); index>=0; index--){
		this->offsprings[index]->fitness = fitnessTemp[index];
		this->offsprings[index]->valid = true;
	}	  
 
        first_generation = false;
        delete[](fitnessTemp);
}





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void ParametersImpl::setDefaultParameters(int argc, char** argv){
        this->minimizing = \MINIMAXI;
        this->nbGen = setVariable("nbGen",(int)\NB_GEN);

        seed = setVariable("seed",(int)time(0));
        globalRandomGenerator = new CRandomGenerator(seed);
        this->randomGenerator = globalRandomGenerator;

        selectionOperator = getSelectionOperator(setVariable("selectionOperator","\SELECTOR_OPERATOR"), this->minimizing, globalRandomGenerator);
        replacementOperator = getSelectionOperator(setVariable("reduceFinalOperator","\RED_FINAL_OPERATOR"),this->minimizing, globalRandomGenerator);
        parentReductionOperator = getSelectionOperator(setVariable("reduceParentsOperator","\RED_PAR_OPERATOR"),this->minimizing, globalRandomGenerator);
        offspringReductionOperator = getSelectionOperator(setVariable("reduceOffspringOperator","\RED_OFF_OPERATOR"),this->minimizing, globalRandomGenerator);
        selectionPressure = setVariable("selectionPressure",(float)\SELECT_PRM);
        replacementPressure = setVariable("reduceFinalPressure",(float)\RED_FINAL_PRM);
        parentReductionPressure = setVariable("reduceParentsPressure",(float)\RED_PAR_PRM);
        offspringReductionPressure = setVariable("reduceOffspringPressure",(float)\RED_OFF_PRM);
        pCrossover = \XOVER_PROB;
        pMutation = \MUT_PROB;
        pMutationPerGene = 0.05;

        parentPopulationSize = setVariable("popSize",(int)\POP_SIZE);
        offspringPopulationSize = setVariable("nbOffspring",(int)\OFF_SIZE);


        parentReductionSize = setReductionSizes(parentPopulationSize, setVariable("survivingParents",(float)\SURV_PAR_SIZE));
        offspringReductionSize = setReductionSizes(offspringPopulationSize, setVariable("survivingOffspring",(float)\SURV_OFF_SIZE));

        this->elitSize = setVariable("elite",(int)\ELITE_SIZE);
        this->strongElitism = setVariable("eliteType",(int)\ELITISM);

        if((this->parentReductionSize + this->offspringReductionSize) < this->parentPopulationSize){
                printf("*WARNING* parentReductionSize + offspringReductionSize < parentPopulationSize\n");
                printf("*WARNING* change Sizes in .prm or .ez\n");
                printf("EXITING\n");
                exit(1);
        }
        if((this->parentPopulationSize - this->parentReductionSize)>this->parentPopulationSize-this->elitSize){
                printf("*WARNING* parentPopulationSize - parentReductionSize > parentPopulationSize - elitSize\n");
                printf("*WARNING* change Sizes in .prm or .ez\n");
                printf("EXITING\n");
                exit(1);
        }
        if(!this->strongElitism && ((this->offspringPopulationSize - this->offspringReductionSize)>this->offspringPopulationSize-this->elitSize)){
                printf("*WARNING* offspringPopulationSize - offspringReductionSize > offspringPopulationSize - elitSize\n");
                printf("*WARNING* change Sizes in .prm or .ez\n");
                printf("EXITING\n");
                exit(1);
        }
        if(offspringReductionSize<offspringPopulationSize) offspringReduction = true;
        else offspringReduction = false;

        if(parentReductionSize<parentPopulationSize) parentReduction = true;
        else parentReduction = false;

        generationalCriterion = new CGenerationalCriterion(setVariable("nbGen",(int)\NB_GEN));
        controlCStopingCriterion = new CControlCStopingCriterion();
        timeCriterion = new CTimeCriterion(setVariable("timeLimit",\TIME_LIMIT));

	this->optimise=0;

        this->printStats = setVariable("printStats",\PRINT_STATS);
        this->generateCSVFile = setVariable("generateCSVFile",\GENERATE_CSV_FILE);
        this->generateGnuplotScript = setVariable("generateGnuplotScript",\GENERATE_GNUPLOT_SCRIPT);
        this->generateRScript = setVariable("generateRScript",\GENERATE_R_SCRIPT);
        this->plotStats = setVariable("plotStats",\PLOT_STATS);
	this->printInitialPopulation = setVariable("printInitialPopulation",0);
	this->printFinalPopulation = setVariable("printFinalPopulation",0);
	this->savePopulation = setVariable("savePopulation",\SAVE_POPULATION);
	this->startFromFile = setVariable("startFromFile",\START_FROM_FILE);

        this->outputFilename = (char*)"EASEA";
        this->plotOutputFilename = (char*)"EASEA.png";

	this->remoteIslandModel = setVariable("remoteIslandModel",\REMOTE_ISLAND_MODEL);
        this->ipFile = (char*)setVariable("ipFile","\IP_FILE").c_str();
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        this->migrationProbability = setVariable("migrationProbability",(float)\MIGRATION_PROBABILITY);
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}

CEvolutionaryAlgorithm* ParametersImpl::newEvolutionaryAlgorithm(){

	pEZ_MUT_PROB = &pMutationPerGene;
	pEZ_XOVER_PROB = &pCrossover;
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	EZ_NB_GEN = (unsigned*)setVariable("nbGen",\NB_GEN);
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	EZ_current_generation=0;

	CEvolutionaryAlgorithm* ea = new EvolutionaryAlgorithmImpl(this);
	generationalCriterion->setCounterEa(ea->getCurrentGenerationPtr());
	 ea->addStoppingCriterion(generationalCriterion);
	ea->addStoppingCriterion(controlCStopingCriterion);
	ea->addStoppingCriterion(timeCriterion);

	  EZ_NB_GEN=((CGenerationalCriterion*)ea->stoppingCriteria[0])->getGenerationalLimit();
	  EZ_current_generation=&(ea->currentGeneration);

	 return ea;
}

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inline void IndividualImpl::copyToCudaBuffer(void* buffer, unsigned id){
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 memcpy(((IndividualImpl*)buffer)+id,this,sizeof(IndividualImpl)); 
  
}

void EvolutionaryAlgorithmImpl::initializeParentPopulation(){
    //DEBUG_PRT("Creation of %lu/%lu parents (other could have been loaded from input file)",this->params->parentPopulationSize-this->params->actualParentPopulationSize,this->params->parentPopulationSize);
    int index,Size = this->params->parentPopulationSize;
    
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    if(this->params->startFromFile){
          ifstream AESAE_File("EASEA.pop");
          string AESAE_Line;
          for( index=(Size-1); index>=0; index--) {
             getline(AESAE_File, AESAE_Line);
            this->population->addIndividualParentPopulation(new IndividualImpl(),index);
            ((IndividualImpl*)this->population->parents[index])->deserialize(AESAE_Line);
            ((IndividualImpl*)this->population->parents[index])->copyToCudaBuffer(((PopulationImpl*)this->population)->cuda->cudaBuffer,index);
         }

        }
        else{
                for( index=(Size-1); index>=0; index--) {
                         this->population->addIndividualParentPopulation(new IndividualImpl(),index);
                        ((IndividualImpl*)this->population->parents[index])->copyToCudaBuffer(((PopulationImpl*)this->population)->cuda->cudaBuffer,index);
                }
    }
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    this->population->actualOffspringPopulationSize = 0;
    this->population->actualParentPopulationSize = Size;
}


EvolutionaryAlgorithmImpl::EvolutionaryAlgorithmImpl(Parameters* params) : CEvolutionaryAlgorithm(params){
	this->population = (CPopulation*)new PopulationImpl(this->params->parentPopulationSize,this->params->offspringPopulationSize, this->params->pCrossover,this->params->pMutation,this->params->pMutationPerGene,this->params->randomGenerator,this->params);
	((PopulationImpl*)this->population)->cuda = new CCuda(params->parentPopulationSize, params->offspringPopulationSize, sizeof(IndividualImpl));
	Pop = ((PopulationImpl*)this->population);
	;
}

EvolutionaryAlgorithmImpl::~EvolutionaryAlgorithmImpl(){

}

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PopulationImpl::PopulationImpl(unsigned parentPopulationSize, unsigned offspringPopulationSize, float pCrossover, float pMutation, float pMutationPerGene, CRandomGenerator* rg, Parameters* params) : CPopulation(parentPopulationSize, offspringPopulationSize, pCrossover, pMutation, pMutationPerGene, rg, params){
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	;
}

PopulationImpl::~PopulationImpl(){
}


\START_CUDA_GENOME_H_TPL

#ifndef PROBLEM_DEP_H
#define PROBLEM_DEP_H

//#include "CRandomGenerator.h"
#include <stdlib.h>
#include <iostream>
#include <CIndividual.h>
#include <Parameters.h>
#include <CCuda.h>
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#include <string>

using namespace std;

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class CRandomGenerator;
class CSelectionOperator;
class CGenerationalCriterion;
class CEvolutionaryAlgorithm;
class CPopulation;
class Parameters;
class CCuda;


\INSERT_USER_CLASSES_DEFINITIONS

class IndividualImpl : public CIndividual {

public: // in EASEA the genome is public (for user functions,...)
	// Class members
  	\INSERT_GENOME


public:
	IndividualImpl();
	IndividualImpl(const IndividualImpl& indiv);
	virtual ~IndividualImpl();
	float evaluate();
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	static unsigned getCrossoverArrity(){ return 2; }
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	float getFitness(){ return this->fitness; }
	CIndividual* crossover(CIndividual** p2);
	void printOn(std::ostream& O) const;
	CIndividual* clone();

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	unsigned mutate(float pMutationPerGene);
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	string serialize();
	void deserialize(string AESAE_Line);
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	void copyToCudaBuffer(void* buffer, unsigned id);
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	friend std::ostream& operator << (std::ostream& O, const IndividualImpl& B) ;
	void initRandomGenerator(CRandomGenerator* rg){ IndividualImpl::rg = rg;}
};


class ParametersImpl : public Parameters {
public:
	void setDefaultParameters(int argc, char** argv);
	CEvolutionaryAlgorithm* newEvolutionaryAlgorithm();
};

/**
 * @TODO ces functions devraient s'appeler weierstrassInit, weierstrassFinal etc... (en gros EASEAFinal dans le tpl).
 *
 */

void EASEAInit(int argc, char** argv);
void EASEAFinal(CPopulation* pop);
void EASEABeginningGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm);
void EASEAEndGenerationFunction(CEvolutionaryAlgorithm* evolutionaryAlgorithm);
void EASEAGenerationFunctionBeforeReplacement(CEvolutionaryAlgorithm* evolutionaryAlgorithm);


class EvolutionaryAlgorithmImpl: public CEvolutionaryAlgorithm {
public:
	EvolutionaryAlgorithmImpl(Parameters* params);
	virtual ~EvolutionaryAlgorithmImpl();
	void initializeParentPopulation();
};

class PopulationImpl: public CPopulation {
public:
	CCuda *cuda;
public:
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	PopulationImpl(unsigned parentPopulationSize, unsigned offspringPopulationSize, float pCrossover, float pMutation, float pMutationPerGene, CRandomGenerator* rg, Parameters* params);
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        virtual ~PopulationImpl();
        void evaluateParentPopulation();
	void evaluateOffspringPopulation();
};

#endif /* PROBLEM_DEP_H */

\START_CUDA_MAKEFILE_TPL
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NVCC= nvcc
CPPC= g++
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LIBAESAE=$(EZ_PATH)libeasea/
CXXFLAGS+=-g -Wall -O2 -I$(LIBAESAE)include -I$(EZ_PATH)boost
LDFLAGS=$(EZ_PATH)boost/program_options.a $(LIBAESAE)libeasea.a -lpthread
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EASEA_SRC= EASEAIndividual.cpp
EASEA_MAIN_HDR= EASEA.cpp
EASEA_UC_HDR= EASEAIndividual.hpp

EASEA_HDR= $(EASEA_SRC:.cpp=.hpp) 

SRC= $(EASEA_SRC) $(EASEA_MAIN_HDR)
CUDA_SRC = EASEAIndividual.cu
HDR= $(EASEA_HDR) $(EASEA_UC_HDR)
OBJ= $(EASEA_SRC:.cpp=.o) $(EASEA_MAIN_HDR:.cpp=.o)

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#USER MAKEFILE OPTIONS :
\INSERT_MAKEFILE_OPTION#END OF USER MAKEFILE OPTIONS

CPPFLAGS+= -I$(LIBAESAE)include -I$(EZ_PATH)boost
NVCCFLAGS+= --compiler-options -fpermissive


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BIN= EASEA
  
all:$(BIN)

$(BIN):$(OBJ)
	$(NVCC) $^ -o $@ $(LDFLAGS) 

%.o:%.cu
	$(NVCC) $(NVCCFLAGS) -o $@ $< -c -DTIMING $(CPPFLAGS) -g -Xcompiler -Wall

easeaclean: clean
	rm -f Makefile EASEA.prm $(SRC) $(HDR) EASEA.mak $(CUDA_SRC) *.linkinfo EASEA.png EASEA.dat EASEA.vcproj EASEA.plot EASEA.r EASEA.csv EASEA.pop
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clean:
	rm -f $(OBJ) $(BIN) 	
	
\START_VISUAL_TPL<?xml version="1.0" encoding="Windows-1252"?>
<VisualStudioProject
	ProjectType="Visual C++"
	Version="9,00"
	Name="EASEA"
	ProjectGUID="{E73D5A89-F262-4F0E-A876-3CF86175BC30}"
	RootNamespace="EASEA"
	Keyword="WIN32Proj"
	TargetFrameworkVersion="196613"
	>
	<Platforms>
		<Platform
			Name="WIN32"
		/>
	</Platforms>
	<ToolFiles>
		<ToolFile
			RelativePath="\CUDA_RULE_DIRcommon\Cuda.rules"
		/>
	</ToolFiles>
	<Configurations>
		<Configuration
			Name="Release|WIN32"
			OutputDirectory="$(SolutionDir)"
			IntermediateDirectory="$(ConfigurationName)"
			ConfigurationType="1"
			CharacterSet="1"
			WholeProgramOptimization="1"
			>
			<Tool
				Name="VCPreBuildEventTool"
			/>
			<Tool
				Name="VCCustomBuildTool"
			/>
			<Tool
				Name="CUDA Build Rule"
				Include="\EZ_PATHlibEasea"
				Keep="false"
				Runtime="0"
			/>
			<Tool
				Name="VCXMLDataGeneratorTool"
			/>
			<Tool
				Name="VCWebServiceProxyGeneratorTool"
			/>
			<Tool
				Name="VCMIDLTool"
			/>
			<Tool
				Name="VCCLCompilerTool"
				Optimization="2"
				EnableIntrinsicFunctions="true"
				AdditionalIncludeDirectories="&quot;\EZ_PATHlibEasea&quot;"
				PreprocessorDefinitions="WIN32;NDEBUG;_CONSOLE"
				RuntimeLibrary="0"
				EnableFunctionLevelLinking="true"
				UsePrecompiledHeader="0"
				WarningLevel="3"
				DebugInformationFormat="3"
			/>
			<Tool
				Name="VCManagedResourceCompilerTool"
			/>
			<Tool
				Name="VCResourceCompilerTool"
			/>
			<Tool
				Name="VCPreLinkEventTool"
			/>
			<Tool
				Name="VCLinkerTool"
				AdditionalDependencies="$(CUDA_LIB_PATH)\cudart.lib"
				LinkIncremental="1"
				AdditionalLibraryDirectories="&quot;\EZ_PATHlibEasea&quot;"
				GenerateDebugInformation="true"
				SubSystem="1"
				OptimizeReferences="2"
				EnableCOMDATFolding="2"
				TargetMachine="1"
			/>
			<Tool
				Name="VCALinkTool"
			/>
			<Tool
				Name="VCManifestTool"
			/>
			<Tool
				Name="VCXDCMakeTool"
			/>
			<Tool
				Name="VCBscMakeTool"
			/>
			<Tool
				Name="VCFxCopTool"
			/>
			<Tool
				Name="VCAppVerifierTool"
			/>
			<Tool
				Name="VCPostBuildEventTool"
			/>
		</Configuration>
	</Configurations>
	<References>
	</References>
	<Files>
		<Filter
			Name="Source Files"
			Filter="cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx"
			UniqueIdentifier="{4FC737F1-C7A5-4376-A066-2A32D752A2FF}"
			>
			<File
				RelativePath=".\EASEA.cpp"
				>
			</File>
			<File
				RelativePath=".\EASEAIndividual.cu"
				>
			</File>
		</Filter>
		<Filter
			Name="Header Files"
			Filter="h;hpp;hxx;hm;inl;inc;xsd"
			UniqueIdentifier="{93995380-89BD-4b04-88EB-625FBE52EBFB}"
			>
			<File
				RelativePath=".\EASEAIndividual.hpp"
				>
			</File>
		</Filter>
		<Filter
			Name="Resource Files"
			Filter="rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav"
			UniqueIdentifier="{67DA6AB6-F800-4c08-8B7A-83BB121AAD01}"
			>
		</Filter>
	</Files>
	<Globals>
	</Globals>
</VisualStudioProject>

\START_EO_PARAM_TPL#****************************************
#
#  EASEA.prm
#
#  Parameter file generated by CUDA.tpl AESAE v1.0
#
#***************************************
# --seed=0   # -S : Random number seed. It is possible to give a specific seed.

######    Evolution Engine    ######
--popSize=\POP_SIZE # -P : Population Size
--nbOffspring=\OFF_SIZE # -O : Nb of offspring (percentage or absolute)

######    Stopping Criterions    #####
--nbGen=\NB_GEN #Nb of generations
--timeLimit=\TIME_LIMIT # Time Limit: desactivate with (0) (in Seconds)

######    Evolution Engine / Replacement    ######
--elite=\ELITE_SIZE  # Nb of elite parents (absolute)
--eliteType=\ELITISM # Strong (1) or weak (0) elitism (set elite to 0 for none)
--survivingParents=\SURV_PAR_SIZE # Nb of surviving parents (percentage or absolute)
--survivingOffspring=\SURV_OFF_SIZE  # Nb of surviving offspring (percentage or absolute)
--selectionOperator=\SELECTOR_OPERATOR # Selector: Deterministic, Tournament, Random, Roulette
--selectionPressure=\SELECT_PRM
--reduceParentsOperator=\RED_PAR_OPERATOR
--reduceParentsPressure=\RED_PAR_PRM
--reduceOffspringOperator=\RED_OFF_OPERATOR
--reduceOffspringPressure=\RED_OFF_PRM
--reduceFinalOperator=\RED_FINAL_OPERATOR
--reduceFinalPressure=\RED_FINAL_PRM

#####   Stats Ouput     #####
--printStats=\PRINT_STATS #print Stats to screen
--plotStats=\PLOT_STATS #plot Stats with gnuplot (requires Gnuplot)
--generateCSVFile=\GENERATE_CSV_FILE
--generateGnuplotScript=\GENERATE_GNUPLOT_SCRIPT
--generateRScript=\GENERATE_R_SCRIPT

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#### Population save    ####
--savePopulation=\SAVE_POPULATION #save population to EASEA.pop file
--startFromFile=\START_FROM_FILE #start optimisation from EASEA.pop file

#### Remote Island Model ####
--remoteIslandModel=\REMOTE_ISLAND_MODEL #To initialize communications with remote AESAE's
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--ipFile=\IP_FILE
--migrationProbability=\MIGRATION_PROBABILITY #Probability to send an individual every generation
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\TEMPLATE_END