starpu-max/src/core/perfmodel/perfmodel_history.c

/* StarPU --- Runtime system for heterogeneous multicore architectures.
 *
 * Copyright (C) 2009-2016  Université de Bordeaux
 * Copyright (C) 2010, 2011, 2012, 2013, 2014, 2015, 2016  CNRS
 * Copyright (C) 2011  Télécom-SudParis
 *
 * StarPU is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published by
 * the Free Software Foundation; either version 2.1 of the License, or (at
 * your option) any later version.
 *
 * StarPU is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 *
 * See the GNU Lesser General Public License in COPYING.LGPL for more details.
 */

#if !defined(_WIN32) || defined(__MINGW32__) || defined(__CYGWIN__)
#include <dirent.h>
#include <sys/stat.h>
#endif
#include <errno.h>
#include <common/config.h>
#ifdef HAVE_UNISTD_H
#include <unistd.h>
#endif
#include <common/utils.h>
#include <core/perfmodel/perfmodel.h>
#include <core/jobs.h>
#include <core/workers.h>
#include <datawizard/datawizard.h>
#include <core/perfmodel/regression.h>
#include <core/perfmodel/multiple_regression.h>
#include <common/config.h>
#include <starpu_parameters.h>
#include <common/uthash.h>
#include <limits.h>

#ifdef STARPU_HAVE_WINDOWS
#include <windows.h>
#endif

#define HASH_ADD_UINT32_T(head,field,add) HASH_ADD(hh,head,field,sizeof(uint32_t),add)
#define HASH_FIND_UINT32_T(head,find,out) HASH_FIND(hh,head,find,sizeof(uint32_t),out)

static struct starpu_perfmodel_arch **arch_combs;
static int current_arch_comb;
static int nb_arch_combs;
static starpu_pthread_rwlock_t arch_combs_mutex;
static int historymaxerror;

/* How many executions a codelet will have to be measured before we
 * consider that calibration will provide a value good enough for scheduling */
unsigned _starpu_calibration_minimum;

struct starpu_perfmodel_history_table
{
	UT_hash_handle hh;
	uint32_t footprint;
	struct starpu_perfmodel_history_entry *history_entry;
};

/* We want more than 10% variance on X to trust regression */
#define VALID_REGRESSION(reg_model) \
	((reg_model)->minx < (9*(reg_model)->maxx)/10 && (reg_model)->nsample >= _starpu_calibration_minimum)

static starpu_pthread_rwlock_t registered_models_rwlock;
static struct _starpu_perfmodel_list *registered_models = NULL;

void _starpu_perfmodel_malloc_per_arch(struct starpu_perfmodel *model, int comb, int nb_impl)
{
	int i;

	model->state->per_arch[comb] = (struct starpu_perfmodel_per_arch*)malloc(nb_impl*sizeof(struct starpu_perfmodel_per_arch));
	for(i = 0; i < nb_impl; i++)
	{
		memset(&model->state->per_arch[comb][i], 0, sizeof(struct starpu_perfmodel_per_arch));
	}
	model->state->nimpls_set[comb] = nb_impl;
}

void _starpu_perfmodel_malloc_per_arch_is_set(struct starpu_perfmodel *model, int comb, int nb_impl)
{
	int i;

	model->state->per_arch_is_set[comb] = (int*)malloc(nb_impl*sizeof(int));
	for(i = 0; i < nb_impl; i++)
	{
		model->state->per_arch_is_set[comb][i] = 0;
	}
}

int _starpu_perfmodel_arch_comb_get(int ndevices, struct starpu_perfmodel_device *devices)
{
	int comb, ncomb;
	ncomb = current_arch_comb;
	for(comb = 0; comb < ncomb; comb++)
	{
		int found = 0;
		if(arch_combs[comb]->ndevices == ndevices)
		{
			int dev1, dev2;
			int nfounded = 0;
			for(dev1 = 0; dev1 < arch_combs[comb]->ndevices; dev1++)
			{
				for(dev2 = 0; dev2 < ndevices; dev2++)
				{
					if(arch_combs[comb]->devices[dev1].type == devices[dev2].type &&
					   arch_combs[comb]->devices[dev1].devid == devices[dev2].devid &&
					   arch_combs[comb]->devices[dev1].ncores == devices[dev2].ncores)
						nfounded++;
				}
			}
			if(nfounded == ndevices)
				found = 1;
		}
		if (found)
			return comb;
	}
	return -1;
}

int starpu_perfmodel_arch_comb_get(int ndevices, struct starpu_perfmodel_device *devices)
{
	int ret;
	STARPU_PTHREAD_RWLOCK_RDLOCK(&arch_combs_mutex);
	ret = _starpu_perfmodel_arch_comb_get(ndevices, devices);
	STARPU_PTHREAD_RWLOCK_UNLOCK(&arch_combs_mutex);
	return ret;
}

int starpu_perfmodel_arch_comb_add(int ndevices, struct starpu_perfmodel_device* devices)
{
	STARPU_PTHREAD_RWLOCK_WRLOCK(&arch_combs_mutex);
	int comb = _starpu_perfmodel_arch_comb_get(ndevices, devices);
	if (comb != -1)
	{
		/* Somebody else added it in between */
		STARPU_PTHREAD_RWLOCK_UNLOCK(&arch_combs_mutex);
		return comb;
	}
	if (current_arch_comb >= nb_arch_combs)
	{
		// We need to allocate more arch_combs
		nb_arch_combs = current_arch_comb+10;
		arch_combs = (struct starpu_perfmodel_arch**) realloc(arch_combs, nb_arch_combs*sizeof(struct starpu_perfmodel_arch*));
	}
	arch_combs[current_arch_comb] = (struct starpu_perfmodel_arch*)malloc(sizeof(struct starpu_perfmodel_arch));
	arch_combs[current_arch_comb]->devices = (struct starpu_perfmodel_device*)malloc(ndevices*sizeof(struct starpu_perfmodel_device));
	arch_combs[current_arch_comb]->ndevices = ndevices;
	int dev;
	for(dev = 0; dev < ndevices; dev++)
	{
		arch_combs[current_arch_comb]->devices[dev].type = devices[dev].type;
		arch_combs[current_arch_comb]->devices[dev].devid = devices[dev].devid;
		arch_combs[current_arch_comb]->devices[dev].ncores = devices[dev].ncores;
	}
	comb = current_arch_comb++;
	STARPU_PTHREAD_RWLOCK_UNLOCK(&arch_combs_mutex);
	return comb;
}

static 	void _free_arch_combs(void)
{
	int i;
	STARPU_PTHREAD_RWLOCK_WRLOCK(&arch_combs_mutex);
	for(i = 0; i < current_arch_comb; i++)
	{
		free(arch_combs[i]->devices);
		free(arch_combs[i]);
	}
	current_arch_comb = 0;
	free(arch_combs);
	STARPU_PTHREAD_RWLOCK_UNLOCK(&arch_combs_mutex);
	STARPU_PTHREAD_RWLOCK_DESTROY(&arch_combs_mutex);
}

int starpu_perfmodel_get_narch_combs()
{
	return current_arch_comb;
}

struct starpu_perfmodel_arch *_starpu_arch_comb_get(int comb)
{
	return arch_combs[comb];
}

size_t _starpu_job_get_data_size(struct starpu_perfmodel *model, struct starpu_perfmodel_arch* arch, unsigned impl, struct _starpu_job *j)
{
	struct starpu_task *task = j->task;
	int comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);

	if (model && model->state->per_arch && comb != -1 && model->state->per_arch[comb] && model->state->per_arch[comb][impl].size_base)
	{
		return model->state->per_arch[comb][impl].size_base(task, arch, impl);
	}
	else if (model && model->size_base)
	{
		return model->size_base(task, impl);
	}
	else
	{
		unsigned nbuffers = STARPU_TASK_GET_NBUFFERS(task);
		size_t size = 0;

		unsigned buffer;
		for (buffer = 0; buffer < nbuffers; buffer++)
		{
			starpu_data_handle_t handle = STARPU_TASK_GET_HANDLE(task, buffer);
			size += _starpu_data_get_size(handle);
		}
		return size;
	}
}

/*
 * History based model
 */
static void insert_history_entry(struct starpu_perfmodel_history_entry *entry, struct starpu_perfmodel_history_list **list, struct starpu_perfmodel_history_table **history_ptr)
{
	struct starpu_perfmodel_history_list *link;
	struct starpu_perfmodel_history_table *table;

	link = (struct starpu_perfmodel_history_list *) malloc(sizeof(struct starpu_perfmodel_history_list));
	link->next = *list;
	link->entry = entry;
	*list = link;

	/* detect concurrency issue */
	//HASH_FIND_UINT32_T(*history_ptr, &entry->footprint, table);
	//STARPU_ASSERT(table == NULL);

	table = (struct starpu_perfmodel_history_table*) malloc(sizeof(*table));
	STARPU_ASSERT(table != NULL);
	table->footprint = entry->footprint;
	table->history_entry = entry;
	HASH_ADD_UINT32_T(*history_ptr, footprint, table);
}

#ifndef STARPU_SIMGRID
static void dump_reg_model(FILE *f, struct starpu_perfmodel *model, int comb, int impl)
{
	struct starpu_perfmodel_per_arch *per_arch_model;

	per_arch_model = &model->state->per_arch[comb][impl];
	struct starpu_perfmodel_regression_model *reg_model;
	reg_model = &per_arch_model->regression;

	/*
	 * Linear Regression model
	 */

	/* Unless we have enough measurements, we put NaN in the file to indicate the model is invalid */
	double alpha = nan(""), beta = nan("");
	if (model->type == STARPU_REGRESSION_BASED || model->type == STARPU_NL_REGRESSION_BASED)
	{
		if (reg_model->nsample > 1)
		{
			alpha = reg_model->alpha;
			beta = reg_model->beta;
		}
	}

	fprintf(f, "# sumlnx\tsumlnx2\t\tsumlny\t\tsumlnxlny\talpha\t\tbeta\t\tn\tminx\t\tmaxx\n");
	fprintf(f, "%-15e\t%-15e\t%-15e\t%-15e\t", reg_model->sumlnx, reg_model->sumlnx2, reg_model->sumlny, reg_model->sumlnxlny);
	_starpu_write_double(f, "%-15e", alpha);
	fprintf(f, "\t");
	_starpu_write_double(f, "%-15e", beta);
	fprintf(f, "\t%u\t%-15lu\t%-15lu\n", reg_model->nsample, reg_model->minx, reg_model->maxx);

	/*
	 * Non-Linear Regression model
	 */

	double a = nan(""), b = nan(""), c = nan("");

	if (model->type == STARPU_NL_REGRESSION_BASED)
		_starpu_regression_non_linear_power(per_arch_model->list, &a, &b, &c);

	fprintf(f, "# a\t\tb\t\tc\n");
	_starpu_write_double(f, "%-15e", a);
	fprintf(f, "\t");
	_starpu_write_double(f, "%-15e", b);
	fprintf(f, "\t");
	_starpu_write_double(f, "%-15e", c);
	fprintf(f, "\n");

	/*
	 * Multiple Regression Model
	 */

	if (model->type == STARPU_MULTIPLE_REGRESSION_BASED)
	{
		if (reg_model->ncoeff==0)
			reg_model->ncoeff = model->ncombinations + 1;

		reg_model->coeff = (double *) malloc(reg_model->ncoeff*sizeof(double));
		_starpu_multiple_regression(per_arch_model->list, reg_model->coeff, reg_model->ncoeff, model->nparameters, model->parameters_names, model->combinations, model->symbol);

		fprintf(f, "# n\tintercept\t");
	        unsigned i, j;
		int first;
		for (i=0; i < model->ncombinations; i++)
		{
			if (model->parameters_names == NULL)
				fprintf(f, "c%d", i+1);
			else
			{
				first=1;
				for(j=0; j < model->nparameters; j++)
				{
					if (model->combinations[i][j] > 0)
					{
						if (first)
							first=0;
						else
							fprintf(f, "*");
						
						if(model->parameters_names[j]!= NULL)
							fprintf(f, "%s", model->parameters_names[j]);
						else
							fprintf(f, "P%d", j);
						
						if (model->combinations[i][j] > 1)
							fprintf(f, "^%d", model->combinations[i][j]);
					}
				}
			}
			fprintf(f, "\t\t");
		}

		fprintf(f, "\n%u", reg_model->ncoeff);
		for (i=0; i < reg_model->ncoeff; i++)
			fprintf(f, "\t%-15e", reg_model->coeff[i]);

	}
}
#endif

static void scan_reg_model(FILE *f, struct starpu_perfmodel_regression_model *reg_model, enum starpu_perfmodel_type model_type)
{
	int res;

	/*
	 * Linear Regression model
	 */

	_starpu_drop_comments(f);

	res = fscanf(f, "%le\t%le\t%le\t%le\t", &reg_model->sumlnx, &reg_model->sumlnx2, &reg_model->sumlny, &reg_model->sumlnxlny);
	STARPU_ASSERT_MSG(res == 4, "Incorrect performance model file");
	res = _starpu_read_double(f, "%le", &reg_model->alpha);
	STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");
	res = _starpu_read_double(f, "\t%le", &reg_model->beta);
	STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");
	res = fscanf(f, "\t%u\t%lu\t%lu\n", &reg_model->nsample, &reg_model->minx, &reg_model->maxx);
	STARPU_ASSERT_MSG(res == 3, "Incorrect performance model file");

	/* If any of the parameters describing the linear regression model is NaN, the model is invalid */
	unsigned invalid = (isnan(reg_model->alpha)||isnan(reg_model->beta));
	reg_model->valid = !invalid && VALID_REGRESSION(reg_model);

	/*
	 * Non-Linear Regression model
	 */

	_starpu_drop_comments(f);

	res = _starpu_read_double(f, "%le", &reg_model->a);
	STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");
	res = _starpu_read_double(f, "\t%le", &reg_model->b);
	STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");
	res = _starpu_read_double(f, "%le", &reg_model->c);
	STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");
	res = fscanf(f, "\n");
	STARPU_ASSERT_MSG(res == 0, "Incorrect performance model file");

	/* If any of the parameters describing the non-linear regression model is NaN, the model is invalid */
	unsigned nl_invalid = (isnan(reg_model->a)||isnan(reg_model->b)||isnan(reg_model->c));
	reg_model->nl_valid = !nl_invalid && VALID_REGRESSION(reg_model);

	/*
	 * Multiple Regression Model
	 */
	if (model_type == STARPU_MULTIPLE_REGRESSION_BASED)
	{
		_starpu_drop_comments(f);

		// Read how many coefficients is there
		res = fscanf(f, "%u", &reg_model->ncoeff);
		STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");

		reg_model->coeff = malloc(reg_model->ncoeff*sizeof(double));

		unsigned multi_invalid = 0;
		unsigned i;
		for (i=0; i < reg_model->ncoeff; i++)
		{
			res = _starpu_read_double(f, "%le", &reg_model->coeff[i]);
			STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");
			multi_invalid = (multi_invalid||isnan(reg_model->coeff[i]));
		}
		reg_model->multi_valid = !multi_invalid;
	}
}


#ifndef STARPU_SIMGRID
static void dump_history_entry(FILE *f, struct starpu_perfmodel_history_entry *entry)
{
	fprintf(f, "%08x\t%-15lu\t%-15e\t%-15e\t%-15e\t%-15e\t%-15e\t%u\n", entry->footprint, (unsigned long) entry->size, entry->flops, entry->mean, entry->deviation, entry->sum, entry->sum2, entry->nsample);
}
#endif

static void scan_history_entry(FILE *f, struct starpu_perfmodel_history_entry *entry)
{
	int res;

	_starpu_drop_comments(f);

	/* In case entry is NULL, we just drop these values */
	unsigned nsample;
	uint32_t footprint;
	unsigned long size; /* in bytes */
	double flops;
	double mean;
	double deviation;
	double sum;
	double sum2;

	char line[256];
	char *ret;

	ret = fgets(line, sizeof(line), f);
	STARPU_ASSERT(ret);
	STARPU_ASSERT(strchr(line, '\n'));

	/* Read the values from the file */
	res = sscanf(line, "%x\t%lu\t%le\t%le\t%le\t%le\t%le\t%u", &footprint, &size, &flops, &mean, &deviation, &sum, &sum2, &nsample);

	if (res != 8)
	{
		flops = 0.;
		/* Read the values from the file */
		res = sscanf(line, "%x\t%lu\t%le\t%le\t%le\t%le\t%u", &footprint, &size, &mean, &deviation, &sum, &sum2, &nsample);
		STARPU_ASSERT_MSG(res == 7, "Incorrect performance model file");
	}

	if (entry)
	{
		entry->footprint = footprint;
		entry->size = size;
		entry->flops = flops;
		entry->mean = mean;
		entry->deviation = deviation;
		entry->sum = sum;
		entry->sum2 = sum2;
		entry->nsample = nsample;
	}
}

static void parse_per_arch_model_file(FILE *f, struct starpu_perfmodel_per_arch *per_arch_model, unsigned scan_history, enum starpu_perfmodel_type model_type)
{
	unsigned nentries;

	_starpu_drop_comments(f);

	int res = fscanf(f, "%u\n", &nentries);
	STARPU_ASSERT_MSG(res == 1, "Incorrect performance model file");

	scan_reg_model(f, &per_arch_model->regression, model_type);

	/* parse entries */
	unsigned i;
	for (i = 0; i < nentries; i++)
	{
		struct starpu_perfmodel_history_entry *entry = NULL;
		if (scan_history)
		{
			entry = (struct starpu_perfmodel_history_entry *) malloc(sizeof(struct starpu_perfmodel_history_entry));
			STARPU_ASSERT(entry);

			/* Tell  helgrind that we do not care about
			 * racing access to the sampling, we only want a
			 * good-enough estimation */
			STARPU_HG_DISABLE_CHECKING(entry->nsample);
			STARPU_HG_DISABLE_CHECKING(entry->mean);
			entry->nerror = 0;
		}

		scan_history_entry(f, entry);

		/* insert the entry in the hashtable and the list structures  */
		/* TODO: Insert it at the end of the list, to avoid reversing
		 * the order... But efficiently! We may have a lot of entries */
		if (scan_history)
			insert_history_entry(entry, &per_arch_model->list, &per_arch_model->history);
	}
}


static void parse_arch(FILE *f, struct starpu_perfmodel *model, unsigned scan_history, int comb)
{
	struct starpu_perfmodel_per_arch dummy;
	unsigned nimpls, implmax, impl, i, ret;

	/* Parsing number of implementation */
	_starpu_drop_comments(f);
	ret = fscanf(f, "%u\n", &nimpls);
	STARPU_ASSERT_MSG(ret == 1, "Incorrect performance model file");

	if( model != NULL)
	{
		/* Parsing each implementation */
		implmax = STARPU_MIN(nimpls, STARPU_MAXIMPLEMENTATIONS);
		model->state->nimpls[comb] = implmax;
		if (!model->state->per_arch[comb])
		{
			_starpu_perfmodel_malloc_per_arch(model, comb, STARPU_MAXIMPLEMENTATIONS);
		}
		if (!model->state->per_arch_is_set[comb])
		{
			_starpu_perfmodel_malloc_per_arch_is_set(model, comb, STARPU_MAXIMPLEMENTATIONS);
		}

		for (impl = 0; impl < implmax; impl++)
		{
			struct starpu_perfmodel_per_arch *per_arch_model = &model->state->per_arch[comb][impl];
			model->state->per_arch_is_set[comb][impl] = 1;
			parse_per_arch_model_file(f, per_arch_model, scan_history, model->type);
		}
	}
	else
	{
		impl = 0;
	}

	/* if the number of implementation is greater than STARPU_MAXIMPLEMENTATIONS
	 * we skip the last implementation */
	for (i = impl; i < nimpls; i++)
		parse_per_arch_model_file(f, &dummy, 0, model->type);

}

static enum starpu_worker_archtype _get_enum_type(int type)
{
	switch(type)
	{
	        case 0:
			return STARPU_CPU_WORKER;
        	case 1:
			return STARPU_CUDA_WORKER;
	        case 2:
			return STARPU_OPENCL_WORKER;
        	case 3:
			return STARPU_MIC_WORKER;
        	case 4:
			return STARPU_SCC_WORKER;
		default:
			STARPU_ABORT();
	}

}

static void parse_comb(FILE *f, struct starpu_perfmodel *model, unsigned scan_history, int comb)
{
	int ndevices = 0;
	_starpu_drop_comments(f);
	int ret = fscanf(f, "%d\n", &ndevices );
	STARPU_ASSERT_MSG(ret == 1, "Incorrect performance model file");

	struct starpu_perfmodel_device devices[ndevices];

	int dev;
	for(dev = 0; dev < ndevices; dev++)
	{
		enum starpu_worker_archtype dev_type;
		_starpu_drop_comments(f);
		int type;
		ret = fscanf(f, "%d\n", &type);
		STARPU_ASSERT_MSG(ret == 1, "Incorrect performance model file");
		dev_type = _get_enum_type(type);
		int dev_id;
		_starpu_drop_comments(f);
		ret = fscanf(f, "%d\n", &dev_id);
		STARPU_ASSERT_MSG(ret == 1, "Incorrect performance model file");
		int ncores;
		_starpu_drop_comments(f);
		ret = fscanf(f, "%d\n", &ncores);
		STARPU_ASSERT_MSG(ret == 1, "Incorrect performance model file");
		devices[dev].type = dev_type;
		devices[dev].devid = dev_id;
		devices[dev].ncores = ncores;
	}
	int id_comb = starpu_perfmodel_arch_comb_get(ndevices, devices);
	if(id_comb == -1)
		id_comb = starpu_perfmodel_arch_comb_add(ndevices, devices);

	model->state->combs[comb] = id_comb;
	parse_arch(f, model, scan_history, id_comb);
}

static void parse_model_file(FILE *f, struct starpu_perfmodel *model, unsigned scan_history)
{
	int ret, version=0;

	/* Parsing performance model version */
	_starpu_drop_comments(f);
	ret = fscanf(f, "%d\n", &version);
	STARPU_ASSERT_MSG(version == _STARPU_PERFMODEL_VERSION, "Incorrect performance model file with a model version %d not being the current model version (%d)\n",
			  version, _STARPU_PERFMODEL_VERSION);
	STARPU_ASSERT_MSG(ret == 1, "Incorrect performance model file");

	int ncombs = 0;
	_starpu_drop_comments(f);
	ret = fscanf(f, "%d\n", &ncombs);
	STARPU_ASSERT_MSG(ret == 1, "Incorrect performance model file");
	if(ncombs > 0)
	{
		model->state->ncombs = ncombs;
	}

	if (ncombs >= model->state->ncombs_set)
	{
		// The model has more combs than the original number of arch_combs, we need to reallocate
		_starpu_perfmodel_realloc(model, ncombs);
	}

	int comb;
	for(comb = 0; comb < ncombs; comb++)
		parse_comb(f, model, scan_history, comb);
}

#ifndef STARPU_SIMGRID
static void dump_per_arch_model_file(FILE *f, struct starpu_perfmodel *model, int comb, unsigned impl)
{
	struct starpu_perfmodel_per_arch *per_arch_model;

	per_arch_model = &model->state->per_arch[comb][impl];
	/* count the number of elements in the lists */
	struct starpu_perfmodel_history_list *ptr = NULL;
	unsigned nentries = 0;

	if (model->type == STARPU_HISTORY_BASED || model->type == STARPU_NL_REGRESSION_BASED)
	{
		/* Dump the list of all entries in the history */
		ptr = per_arch_model->list;
		while(ptr)
		{
			nentries++;
			ptr = ptr->next;
		}
	}

	/* header */
	char archname[32];
	starpu_perfmodel_get_arch_name(arch_combs[comb], archname,  32, impl);
	fprintf(f, "#####\n");
	fprintf(f, "# Model for %s\n", archname);
	fprintf(f, "# number of entries\n%u\n", nentries);

	dump_reg_model(f, model, comb, impl);

	/* Dump the history into the model file in case it is necessary */
	if (model->type == STARPU_HISTORY_BASED || model->type == STARPU_NL_REGRESSION_BASED)
	{
		fprintf(f, "# hash\t\tsize\t\tflops\t\tmean (us)\tdev (us)\tsum\t\tsum2\t\tn\n");
		ptr = per_arch_model->list;
		while (ptr)
		{
			dump_history_entry(f, ptr->entry);
			ptr = ptr->next;
		}
	}

	fprintf(f, "\n");
}

static void dump_model_file(FILE *f, struct starpu_perfmodel *model)
{
	fprintf(f, "##################\n");
	fprintf(f, "# Performance Model Version\n");
	fprintf(f, "%d\n\n", _STARPU_PERFMODEL_VERSION);

	int ncombs = model->state->ncombs;

	fprintf(f, "####################\n");
	fprintf(f, "# COMBs\n");
	fprintf(f, "# number of combinations\n");
	fprintf(f, "%u\n", ncombs);

	int i, impl, dev;
	for(i = 0; i < ncombs; i++)
	{
		int comb = model->state->combs[i];
		int ndevices = arch_combs[comb]->ndevices;
		fprintf(f, "####################\n");
		fprintf(f, "# COMB_%d\n", comb);
		fprintf(f, "# number of types devices\n");
		fprintf(f, "%u\n", ndevices);

		for(dev = 0; dev < ndevices; dev++)
		{
			fprintf(f, "####################\n");
			fprintf(f, "# DEV_%d\n", dev);
			fprintf(f, "# device type (CPU - 0, CUDA - 1, OPENCL - 2, MIC - 3, SCC - 4)\n");
			fprintf(f, "%u\n", arch_combs[comb]->devices[dev].type);

			fprintf(f, "####################\n");
			fprintf(f, "# DEV_%d\n", dev);
			fprintf(f, "# device id \n");
			fprintf(f, "%u\n", arch_combs[comb]->devices[dev].devid);

			fprintf(f, "####################\n");
			fprintf(f, "# DEV_%d\n", dev);
			fprintf(f, "# number of cores \n");
			fprintf(f, "%u\n", arch_combs[comb]->devices[dev].ncores);
		}

		int nimpls = model->state->nimpls[comb];
		fprintf(f, "##########\n");
		fprintf(f, "# number of implementations\n");
		fprintf(f, "%u\n", nimpls);
		for (impl = 0; impl < nimpls; impl++)
		{
			dump_per_arch_model_file(f, model, comb, impl);
		}
	}
}
#endif

void _starpu_perfmodel_realloc(struct starpu_perfmodel *model, int nb)
{
	int i;

	STARPU_ASSERT(nb > model->state->ncombs_set);
#ifdef SSIZE_MAX
	STARPU_ASSERT((size_t) nb < SSIZE_MAX / sizeof(struct starpu_perfmodel_per_arch*));
#endif
	model->state->per_arch = (struct starpu_perfmodel_per_arch**) realloc(model->state->per_arch, nb*sizeof(struct starpu_perfmodel_per_arch*));
	model->state->per_arch_is_set = (int**) realloc(model->state->per_arch_is_set, nb*sizeof(int*));
	model->state->nimpls = (int *)realloc(model->state->nimpls, nb*sizeof(int));
	model->state->nimpls_set = (int *)realloc(model->state->nimpls_set, nb*sizeof(int));
	model->state->combs = (int*)realloc(model->state->combs, nb*sizeof(int));
	for(i = model->state->ncombs_set; i < nb; i++)
	{
		model->state->per_arch[i] = NULL;
		model->state->per_arch_is_set[i] = NULL;
		model->state->nimpls[i] = 0;
		model->state->nimpls_set[i] = 0;
	}
	model->state->ncombs_set = nb;
}

void starpu_perfmodel_init(struct starpu_perfmodel *model)
{
	int already_init;
	int i, ncombs;

	STARPU_ASSERT(model);

	STARPU_PTHREAD_RWLOCK_RDLOCK(&registered_models_rwlock);
	already_init = model->is_init;
	STARPU_PTHREAD_RWLOCK_UNLOCK(&registered_models_rwlock);

	if (already_init)
		return;

	/* The model is still not loaded so we grab the lock in write mode, and
	 * if it's not loaded once we have the lock, we do load it. */
	STARPU_PTHREAD_RWLOCK_WRLOCK(&registered_models_rwlock);

	/* Was the model initialized since the previous test ? */
	if (model->is_init)
	{
		STARPU_PTHREAD_RWLOCK_UNLOCK(&registered_models_rwlock);
		return;
	}

	model->state = malloc(sizeof(struct _starpu_perfmodel_state));
	STARPU_PTHREAD_RWLOCK_INIT(&model->state->model_rwlock, NULL);

	STARPU_PTHREAD_RWLOCK_RDLOCK(&arch_combs_mutex);
	model->state->ncombs_set = ncombs = nb_arch_combs;
	STARPU_PTHREAD_RWLOCK_UNLOCK(&arch_combs_mutex);
	model->state->per_arch = (struct starpu_perfmodel_per_arch**) malloc(ncombs*sizeof(struct starpu_perfmodel_per_arch*));
	model->state->per_arch_is_set = (int**) malloc(ncombs*sizeof(int*));
	model->state->nimpls = (int *)malloc(ncombs*sizeof(int));
	model->state->nimpls_set = (int *)malloc(ncombs*sizeof(int));
	model->state->combs = (int*)malloc(ncombs*sizeof(int));
	model->state->ncombs = 0;

	for(i = 0; i < ncombs; i++)
	{
		model->state->per_arch[i] = NULL;
		model->state->per_arch_is_set[i] = NULL;
		model->state->nimpls[i] = 0;
		model->state->nimpls_set[i] = 0;
	}

	/* add the model to a linked list */
	struct _starpu_perfmodel_list *node = (struct _starpu_perfmodel_list *) malloc(sizeof(struct _starpu_perfmodel_list));

	node->model = model;
	//model->debug_modelid = debug_modelid++;

	/* put this model at the beginning of the list */
	node->next = registered_models;
	registered_models = node;

	model->is_init = 1;
	STARPU_PTHREAD_RWLOCK_UNLOCK(&registered_models_rwlock);
}

static void get_model_debug_path(struct starpu_perfmodel *model, const char *arch, char *path, size_t maxlen)
{
	STARPU_ASSERT(path);

	char hostname[65];
	_starpu_gethostname(hostname, sizeof(hostname));

	snprintf(path, maxlen, "%s/%s.%s.%s.debug", _starpu_get_perf_model_dir_debug(), model->symbol, hostname, arch);
}

void starpu_perfmodel_get_model_path(const char *symbol, char *path, size_t maxlen)
{
	char hostname[65];
	_starpu_gethostname(hostname, sizeof(hostname));
	const char *dot = strrchr(symbol, '.');

	snprintf(path, maxlen, "%s/%s%s%s", _starpu_get_perf_model_dir_codelet(), symbol, dot?"":".", dot?"":hostname);
}

#ifndef STARPU_SIMGRID
static void save_history_based_model(struct starpu_perfmodel *model)
{
	STARPU_ASSERT(model);
	STARPU_ASSERT(model->symbol);

	/* TODO checks */

	/* filename = $STARPU_PERF_MODEL_DIR/codelets/symbol.hostname */
	char path[256];
	starpu_perfmodel_get_model_path(model->symbol, path, 256);

	_STARPU_DEBUG("Opening performance model file %s for model %s\n", path, model->symbol);

	/* overwrite existing file, or create it */
	FILE *f;
	f = fopen(path, "w+");
	STARPU_ASSERT_MSG(f, "Could not save performance model %s\n", path);

	_starpu_fwrlock(f);
	_starpu_fftruncate(f, 0);
	dump_model_file(f, model);
	_starpu_fwrunlock(f);

	fclose(f);
}
#endif

static void _starpu_dump_registered_models(void)
{
#ifndef STARPU_SIMGRID
	STARPU_PTHREAD_RWLOCK_WRLOCK(&registered_models_rwlock);

	struct _starpu_perfmodel_list *node;
	node = registered_models;

	_STARPU_DEBUG("DUMP MODELS !\n");

	while (node)
	{
		if (node->model->is_init)
			save_history_based_model(node->model);
		node = node->next;
	}

	STARPU_PTHREAD_RWLOCK_UNLOCK(&registered_models_rwlock);
#endif
}

void _starpu_initialize_registered_performance_models(void)
{
	/* make sure the performance model directory exists (or create it) */
	_starpu_create_sampling_directory_if_needed();

	registered_models = NULL;

	STARPU_PTHREAD_RWLOCK_INIT(&registered_models_rwlock, NULL);

	struct _starpu_machine_config *conf = _starpu_get_machine_config();
	unsigned ncores = conf->topology.nhwcpus;
	unsigned ncuda =  conf->topology.nhwcudagpus;
	unsigned nopencl = conf->topology.nhwopenclgpus;
	unsigned nmic = 0;
	unsigned i;
	for(i = 0; i < conf->topology.nhwmicdevices; i++)
		nmic += conf->topology.nhwmiccores[i];
	unsigned nscc = conf->topology.nhwscc;

	// We used to allocate 2**(ncores + ncuda + nopencl + nmic + nscc), this is too big
	// We now allocate only 2*(ncores + ncuda + nopencl + nmic + nscc), and reallocate when necessary in starpu_perfmodel_arch_comb_add
	nb_arch_combs = 2 * (ncores + ncuda + nopencl + nmic + nscc);
	arch_combs = (struct starpu_perfmodel_arch**) malloc(nb_arch_combs*sizeof(struct starpu_perfmodel_arch*));
	current_arch_comb = 0;
	STARPU_PTHREAD_RWLOCK_INIT(&arch_combs_mutex, NULL);
	historymaxerror = starpu_get_env_number_default("STARPU_HISTORY_MAX_ERROR", STARPU_HISTORYMAXERROR);
	_starpu_calibration_minimum = starpu_get_env_number_default("STARPU_CALIBRATE_MINIMUM", 10);
}

void _starpu_deinitialize_performance_model(struct starpu_perfmodel *model)
{
	if(model->is_init && model->state && model->state->per_arch != NULL)
	{
		int i;
		for(i=0 ; i<model->state->ncombs_set ; i++)
		{
			if (model->state->per_arch[i])
			{
				int impl;
				for(impl=0 ; impl<model->state->nimpls_set[i] ; impl++)
				{
					struct starpu_perfmodel_per_arch *archmodel = &model->state->per_arch[i][impl];
					if (archmodel->history)
					{
						struct starpu_perfmodel_history_list *list, *plist;
						struct starpu_perfmodel_history_table *entry, *tmp;

						HASH_ITER(hh, archmodel->history, entry, tmp)
						{
							HASH_DEL(archmodel->history, entry);
							free(entry);
						}
						archmodel->history = NULL;

						list = archmodel->list;
						while (list)
						{
							free(list->entry);
							plist = list;
							list = list->next;
							free(plist);
						}
						archmodel->list = NULL;
					}
				}
				free(model->state->per_arch[i]);
				model->state->per_arch[i] = NULL;

				free(model->state->per_arch_is_set[i]);
				model->state->per_arch_is_set[i] = NULL;
			}
		}
		free(model->state->per_arch);
		model->state->per_arch = NULL;

		free(model->state->per_arch_is_set);
		model->state->per_arch_is_set = NULL;

		free(model->state->nimpls);
		model->state->nimpls = NULL;

		free(model->state->nimpls_set);
		model->state->nimpls_set = NULL;

		free(model->state->combs);
		model->state->combs = NULL;
		model->state->ncombs = 0;
	}

	model->is_init = 0;
	model->is_loaded = 0;
}

void _starpu_deinitialize_registered_performance_models(void)
{
	if (_starpu_get_calibrate_flag())
		_starpu_dump_registered_models();

	STARPU_PTHREAD_RWLOCK_WRLOCK(&registered_models_rwlock);

	struct _starpu_perfmodel_list *node, *pnode;
	node = registered_models;

	_STARPU_DEBUG("FREE MODELS !\n");

	while (node)
	{
		struct starpu_perfmodel *model = node->model;

		STARPU_PTHREAD_RWLOCK_WRLOCK(&model->state->model_rwlock);
		_starpu_deinitialize_performance_model(model);
		STARPU_PTHREAD_RWLOCK_UNLOCK(&model->state->model_rwlock);

		free(node->model->state);
		node->model->state = NULL;

		pnode = node;
		node = node->next;
		free(pnode);
	}
	registered_models = NULL;

	STARPU_PTHREAD_RWLOCK_UNLOCK(&registered_models_rwlock);
	STARPU_PTHREAD_RWLOCK_DESTROY(&registered_models_rwlock);
	_free_arch_combs();
	starpu_perfmodel_free_sampling_directories();
}

/* We first try to grab the global lock in read mode to check whether the model
 * was loaded or not (this is very likely to have been already loaded). If the
 * model was not loaded yet, we take the lock in write mode, and if the model
 * is still not loaded once we have the lock, we do load it.  */
void _starpu_load_history_based_model(struct starpu_perfmodel *model, unsigned scan_history)
{
	STARPU_PTHREAD_RWLOCK_WRLOCK(&model->state->model_rwlock);

	if(!model->is_loaded)
	{
		char path[256];
		// Check if a symbol is defined before trying to load the model from a file
		STARPU_ASSERT_MSG(model->symbol, "history-based performance models must have a symbol");

		starpu_perfmodel_get_model_path(model->symbol, path, 256);

		_STARPU_DEBUG("Opening performance model file %s for model %s ...\n", path, model->symbol);

		unsigned calibrate_flag = _starpu_get_calibrate_flag();
		model->benchmarking = calibrate_flag;
		model->is_loaded = 1;

		if (calibrate_flag == 2)
		{
			/* The user specified that the performance model should
			 * be overwritten, so we don't load the existing file !
			 * */
			_STARPU_DEBUG("Overwrite existing file\n");
		}
		else
		{
			/* We try to load the file */
			FILE *f;
			f = fopen(path, "r");
			if (f)
			{
				_starpu_frdlock(f);
				parse_model_file(f, model, scan_history);
				_starpu_frdunlock(f);
				fclose(f);
				_STARPU_DEBUG("Performance model file %s for model %s is loaded\n", path, model->symbol);
			}
			else
			{
				_STARPU_DEBUG("Performance model file %s does not exist or is not readable\n", path);
			}
		}

	}
	STARPU_PTHREAD_RWLOCK_UNLOCK(&model->state->model_rwlock);

}

void starpu_perfmodel_directory(FILE *output)
{
	fprintf(output, "directory: <%s>\n", _starpu_get_perf_model_dir_codelet());
}

/* This function is intended to be used by external tools that should read
 * the performance model files */
int starpu_perfmodel_list(FILE *output)
{
#if !defined(_WIN32) || defined(__MINGW32__) || defined(__CYGWIN__)
        char *path;
        DIR *dp;
        struct dirent *ep;

	path = _starpu_get_perf_model_dir_codelet();
        dp = opendir(path);
        if (dp != NULL)
	{
                while ((ep = readdir(dp)))
		{
                        if (strcmp(ep->d_name, ".") && strcmp(ep->d_name, ".."))
                                fprintf(output, "file: <%s>\n", ep->d_name);
                }
                closedir (dp);
        }
        else
	{
		_STARPU_DISP("Could not open the perfmodel directory <%s>: %s\n", path, strerror(errno));
        }
	return 0;
#else
	fprintf(stderr,"Listing perfmodels is not implemented on pure Windows yet\n");
	return 1;
#endif
}

/* This function is intended to be used by external tools that should read the
 * performance model files */
/* TODO: write an clear function, to free symbol and history */
int starpu_perfmodel_load_symbol(const char *symbol, struct starpu_perfmodel *model)
{
	model->symbol = strdup(symbol);

	/* where is the file if it exists ? */
	char path[256];
	starpu_perfmodel_get_model_path(model->symbol, path, 256);

	//	_STARPU_DEBUG("get_model_path -> %s\n", path);

	/* does it exist ? */
	int res;
	res = access(path, F_OK);
	if (res)
	{
		const char *dot = strrchr(symbol, '.');
		if (dot)
		{
			char *symbol2 = strdup(symbol);
			symbol2[dot-symbol] = '\0';
			int ret;
			_STARPU_DISP("note: loading history from %s instead of %s\n", symbol2, symbol);
			ret = starpu_perfmodel_load_symbol(symbol2,model);
			free(symbol2);
			return ret;
		}
		_STARPU_DISP("There is no performance model for symbol %s\n", symbol);
		return 1;
	}

	return starpu_perfmodel_load_file(path, model);
}

int starpu_perfmodel_load_file(const char *filename, struct starpu_perfmodel *model)
{
	int res;
	FILE *f = fopen(filename, "r");

	STARPU_ASSERT(f);

	starpu_perfmodel_init(model);

	_starpu_frdlock(f);
	parse_model_file(f, model, 1);
	_starpu_frdunlock(f);

	res = fclose(f);
	STARPU_ASSERT(res == 0);

	return 0;
}

int starpu_perfmodel_unload_model(struct starpu_perfmodel *model)
{
	if (model->symbol)
	{
		free((char *)model->symbol);
		model->symbol = NULL;
	}
	_starpu_deinitialize_performance_model(model);
	return 0;
}

char* starpu_perfmodel_get_archtype_name(enum starpu_worker_archtype archtype)
{
	switch(archtype)
	{
		case(STARPU_CPU_WORKER):
			return "cpu";
			break;
		case(STARPU_CUDA_WORKER):
			return "cuda";
			break;
		case(STARPU_OPENCL_WORKER):
			return "opencl";
			break;
		case(STARPU_MIC_WORKER):
			return "mic";
			break;
		case(STARPU_SCC_WORKER):
			return "scc";
			break;
		default:
			STARPU_ABORT();
			break;
	}
}

void starpu_perfmodel_get_arch_name(struct starpu_perfmodel_arch* arch, char *archname, size_t maxlen,unsigned impl)
{
	int i;
	int comb = _starpu_perfmodel_create_comb_if_needed(arch);

	STARPU_ASSERT(comb != -1);
	char devices[1024];
	int written = 0;
	strcpy(devices, "");
	for(i=0 ; i<arch->ndevices ; i++)
	{
		written += snprintf(devices + written, sizeof(devices)-written, "%s%u%s", starpu_perfmodel_get_archtype_name(arch->devices[i].type), arch->devices[i].devid, i != arch->ndevices-1 ? "_":"");
	}
	snprintf(archname, maxlen, "%s_impl%u (Comb%d)", devices, impl, comb);
}

void starpu_perfmodel_debugfilepath(struct starpu_perfmodel *model,
				    struct starpu_perfmodel_arch* arch, char *path, size_t maxlen, unsigned nimpl)
{
	int comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);
	STARPU_ASSERT(comb != -1);
	char archname[32];
	starpu_perfmodel_get_arch_name(arch, archname, 32, nimpl);

	STARPU_ASSERT(path);

	get_model_debug_path(model, archname, path, maxlen);
}

double _starpu_regression_based_job_expected_perf(struct starpu_perfmodel *model, struct starpu_perfmodel_arch* arch, struct _starpu_job *j, unsigned nimpl)
{
	int comb;
	double exp = NAN;
	size_t size;
	struct starpu_perfmodel_regression_model *regmodel = NULL;

	comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);
	size = _starpu_job_get_data_size(model, arch, nimpl, j);

	if(comb == -1)
		goto docal;
	if (model->state->per_arch[comb] == NULL)
		// The model has not been executed on this combination
		goto docal;

	regmodel = &model->state->per_arch[comb][nimpl].regression;

	if (regmodel->valid && size >= regmodel->minx * 0.9 && size <= regmodel->maxx * 1.1)
                exp = regmodel->alpha*pow((double)size, regmodel->beta);

docal:
	STARPU_HG_DISABLE_CHECKING(model->benchmarking);
	if (isnan(exp) && !model->benchmarking)
	{
		char archname[32];

		starpu_perfmodel_get_arch_name(arch, archname, sizeof(archname), nimpl);
		_STARPU_DISP("Warning: model %s is not calibrated enough for %s size %lu (only %u measurements from size %lu to %lu), forcing calibration for this run. Use the STARPU_CALIBRATE environment variable to control this.\n", model->symbol, archname, (unsigned long) size, regmodel?regmodel->nsample:0, regmodel?regmodel->minx:0, regmodel?regmodel->maxx:0);
		_starpu_set_calibrate_flag(1);
		model->benchmarking = 1;
	}

	return exp;
}

double _starpu_non_linear_regression_based_job_expected_perf(struct starpu_perfmodel *model, struct starpu_perfmodel_arch* arch, struct _starpu_job *j,unsigned nimpl)
{
	int comb;
	double exp = NAN;
	size_t size;
	struct starpu_perfmodel_regression_model *regmodel;
	struct starpu_perfmodel_history_table *entry = NULL;

	comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);
	if(comb == -1)
		goto docal;
	if (model->state->per_arch[comb] == NULL)
		// The model has not been executed on this combination
		goto docal;

	regmodel = &model->state->per_arch[comb][nimpl].regression;
	size = _starpu_job_get_data_size(model, arch, nimpl, j);

	if (regmodel->nl_valid && size >= regmodel->minx * 0.9 && size <= regmodel->maxx * 1.1)
		exp = regmodel->a*pow((double)size, regmodel->b) + regmodel->c;
	else
	{
		uint32_t key = _starpu_compute_buffers_footprint(model, arch, nimpl, j);
		struct starpu_perfmodel_per_arch *per_arch_model = &model->state->per_arch[comb][nimpl];
		struct starpu_perfmodel_history_table *history;

		STARPU_PTHREAD_RWLOCK_RDLOCK(&model->state->model_rwlock);
		history = per_arch_model->history;
		HASH_FIND_UINT32_T(history, &key, entry);
		STARPU_PTHREAD_RWLOCK_UNLOCK(&model->state->model_rwlock);

		/* Here helgrind would shout that this is unprotected access.
		 * We do not care about racing access to the mean, we only want
		 * a good-enough estimation */

		if (entry && entry->history_entry && entry->history_entry->nsample >= _starpu_calibration_minimum)
			exp = entry->history_entry->mean;

docal:
		STARPU_HG_DISABLE_CHECKING(model->benchmarking);
		if (isnan(exp) && !model->benchmarking)
		{
			char archname[32];

			starpu_perfmodel_get_arch_name(arch, archname, sizeof(archname), nimpl);
			_STARPU_DISP("Warning: model %s is not calibrated enough for %s (only %u measurements), forcing calibration for this run. Use the STARPU_CALIBRATE environment variable to control this.\n", model->symbol, archname, entry && entry->history_entry ? entry->history_entry->nsample : 0);
			_starpu_set_calibrate_flag(1);
			model->benchmarking = 1;
		}
	}

	return exp;
}

double _starpu_multiple_regression_based_job_expected_perf(struct starpu_perfmodel *model, struct starpu_perfmodel_arch* arch, struct _starpu_job *j, unsigned nimpl)
{
	int comb;
	double expected_duration=NAN;

	struct starpu_perfmodel_regression_model *reg_model = NULL;
	comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);
	if(comb == -1)
		goto docal;
	if (model->state->per_arch[comb] == NULL)
		// The model has not been executed on this combination
		goto docal;
	reg_model = &model->state->per_arch[comb][nimpl].regression;
	if (reg_model->coeff == NULL)
		goto docal;

	double parameter_value;
	double *parameters;
	parameters = (double *) malloc(model->nparameters*sizeof(double));
	model->parameters(j->task, parameters);
	expected_duration=reg_model->coeff[0];
	unsigned i, k;
	for (i=0; i < model->ncombinations; i++)
	{
		parameter_value=1.;
		for (k=0; k < model->nparameters; k++)
			parameter_value *= pow(parameters[k],model->combinations[i][k]);

		expected_duration += reg_model->coeff[i+1]*parameter_value;
	}

docal:
	STARPU_HG_DISABLE_CHECKING(model->benchmarking);
	if (isnan(expected_duration) && !model->benchmarking)
	{
		char archname[32];

		starpu_perfmodel_get_arch_name(arch, archname, sizeof(archname), nimpl);
		_STARPU_DISP("Warning: model %s is not calibrated enough for %s, forcing calibration for this run. Use the STARPU_CALIBRATE environment variable to control this.\n", model->symbol, archname);
		_starpu_set_calibrate_flag(1);
		model->benchmarking = 1;
	}

	// In the unlikely event that predicted duration is negative
	// in case multiple linear regression is not so accurate
	if (expected_duration < 0 )
		expected_duration = 0.00001;

	//Make sure that the injected time is in milliseconds
	return expected_duration;
}

double _starpu_history_based_job_expected_perf(struct starpu_perfmodel *model, struct starpu_perfmodel_arch* arch, struct _starpu_job *j,unsigned nimpl)
{
	int comb;
	double exp = NAN;
	struct starpu_perfmodel_per_arch *per_arch_model;
	struct starpu_perfmodel_history_entry *entry = NULL;
	struct starpu_perfmodel_history_table *history, *elt;
	uint32_t key;

	comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);
	if(comb == -1)
		goto docal;
	if (model->state->per_arch[comb] == NULL)
		// The model has not been executed on this combination
		goto docal;

	per_arch_model = &model->state->per_arch[comb][nimpl];

	key = _starpu_compute_buffers_footprint(model, arch, nimpl, j);
	STARPU_PTHREAD_RWLOCK_RDLOCK(&model->state->model_rwlock);
	history = per_arch_model->history;
	HASH_FIND_UINT32_T(history, &key, elt);
	entry = (elt == NULL) ? NULL : elt->history_entry;
	STARPU_PTHREAD_RWLOCK_UNLOCK(&model->state->model_rwlock);

	/* Here helgrind would shout that this is unprotected access.
	 * We do not care about racing access to the mean, we only want
	 * a good-enough estimation */

	if (entry && entry->nsample >= _starpu_calibration_minimum)
		/* TODO: report differently if we've scheduled really enough
		 * of that task and the scheduler should perhaps put it aside */
		/* Calibrated enough */
		exp = entry->mean;

docal:
	STARPU_HG_DISABLE_CHECKING(model->benchmarking);
	if (isnan(exp) && !model->benchmarking)
	{
		char archname[32];

		starpu_perfmodel_get_arch_name(arch, archname, sizeof(archname), nimpl);
		_STARPU_DISP("Warning: model %s is not calibrated enough for %s (only %u measurements), forcing calibration for this run. Use the STARPU_CALIBRATE environment variable to control this.\n", model->symbol, archname, entry ? entry->nsample : 0);
		_starpu_set_calibrate_flag(1);
		model->benchmarking = 1;
	}

	return exp;
}

double starpu_perfmodel_history_based_expected_perf(struct starpu_perfmodel *model, struct starpu_perfmodel_arch * arch, uint32_t footprint)
{
	struct _starpu_job j =
		{
			.footprint = footprint,
			.footprint_is_computed = 1,
		};
	return _starpu_history_based_job_expected_perf(model, arch, &j, j.nimpl);
}

int _starpu_perfmodel_create_comb_if_needed(struct starpu_perfmodel_arch* arch)
{
	int comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);
	if(comb == -1)
		comb = starpu_perfmodel_arch_comb_add(arch->ndevices, arch->devices);
	return comb;
}

void _starpu_update_perfmodel_history(struct _starpu_job *j, struct starpu_perfmodel *model, struct starpu_perfmodel_arch* arch, unsigned cpuid STARPU_ATTRIBUTE_UNUSED, double measured, unsigned impl)
{
	if (model)
	{
		int c;
		unsigned found = 0;
		int comb = _starpu_perfmodel_create_comb_if_needed(arch);

		for(c = 0; c < model->state->ncombs; c++)
		{
			if(model->state->combs[c] == comb)
			{
				found = 1;
				break;
			}
		}
		if(!found)
		{
			if (model->state->ncombs + 1 >= model->state->ncombs_set)
			{
				// The number of combinations is bigger than the one which was initially allocated, we need to reallocate,
				// do not only reallocate 1 extra comb, rather reallocate 5 to avoid too frequent calls to _starpu_perfmodel_realloc
				_starpu_perfmodel_realloc(model, model->state->ncombs_set+5);
			}
			model->state->combs[model->state->ncombs++] = comb;
		}

		STARPU_PTHREAD_RWLOCK_WRLOCK(&model->state->model_rwlock);

		if(!model->state->per_arch[comb])
		{
			_starpu_perfmodel_malloc_per_arch(model, comb, STARPU_MAXIMPLEMENTATIONS);
			_starpu_perfmodel_malloc_per_arch_is_set(model, comb, STARPU_MAXIMPLEMENTATIONS);
		}

		struct starpu_perfmodel_per_arch *per_arch_model = &model->state->per_arch[comb][impl];
		if (model->state->per_arch_is_set[comb][impl] == 0)
		{
			// We are adding a new implementation for the given comb and the given impl
			model->state->nimpls[comb]++;
			model->state->per_arch_is_set[comb][impl] = 1;
		}

		if (model->type == STARPU_HISTORY_BASED || model->type == STARPU_NL_REGRESSION_BASED)
		{
			struct starpu_perfmodel_history_entry *entry;
			struct starpu_perfmodel_history_table *elt;
			struct starpu_perfmodel_history_list **list;
			uint32_t key = _starpu_compute_buffers_footprint(model, arch, impl, j);

			list = &per_arch_model->list;

			HASH_FIND_UINT32_T(per_arch_model->history, &key, elt);
			entry = (elt == NULL) ? NULL : elt->history_entry;

			if (!entry)
			{
				/* this is the first entry with such a footprint */
				entry = (struct starpu_perfmodel_history_entry *) malloc(sizeof(struct starpu_perfmodel_history_entry));
				STARPU_ASSERT(entry);

				/* Tell  helgrind that we do not care about
				 * racing access to the sampling, we only want a
				 * good-enough estimation */
				STARPU_HG_DISABLE_CHECKING(entry->nsample);
				STARPU_HG_DISABLE_CHECKING(entry->mean);

				/* Do not take the first measurement into account, it is very often quite bogus */
				/* TODO: it'd be good to use a better estimation heuristic, like the median, or latest n values, etc. */
				entry->mean = 0;
				entry->sum = 0;

				entry->deviation = 0.0;
				entry->sum2 = 0;

				entry->size = _starpu_job_get_data_size(model, arch, impl, j);
				entry->flops = j->task->flops;

				entry->footprint = key;
				entry->nsample = 0;
				entry->nerror = 0;

				insert_history_entry(entry, list, &per_arch_model->history);
			}
			else
			{
				/* There is already an entry with the same footprint */

				double local_deviation = measured/entry->mean;

				if (entry->nsample &&
					(100 * local_deviation > (100 + historymaxerror)
					 || (100 / local_deviation > (100 + historymaxerror))))
				{
					entry->nerror++;

					/* More errors than measurements, we're most probably completely wrong, we flush out all the entries */
					if (entry->nerror >= entry->nsample)
					{
						char archname[32];
						starpu_perfmodel_get_arch_name(arch, archname, sizeof(archname), impl);
						_STARPU_DISP("Too big deviation for model %s on %s: %f vs average %f, %u such errors against %u samples (%+f%%), flushing the performance model. Use the STARPU_HISTORY_MAX_ERROR environement variable to control the threshold (currently %d%%)\n", model->symbol, archname, measured, entry->mean, entry->nerror, entry->nsample, measured * 100. / entry->mean - 100, historymaxerror);
						entry->sum = 0.0;
						entry->sum2 = 0.0;
						entry->nsample = 0;
						entry->nerror = 0;
						entry->mean = 0.0;
						entry->deviation = 0.0;
					}
				}
				else
				{
					entry->sum += measured;
					entry->sum2 += measured*measured;
					entry->nsample++;

					unsigned n = entry->nsample;
					entry->mean = entry->sum / n;
					entry->deviation = sqrt((entry->sum2 - (entry->sum*entry->sum)/n)/n);
				}

				if (j->task->flops != 0.)
				{
					if (entry->flops == 0.)
						entry->flops = j->task->flops;
					else if (((entry->flops - j->task->flops) / entry->flops) > 0.00001)
						/* Incoherent flops! forget about trying to record flops */
						entry->flops = NAN;
				}
			}

			STARPU_ASSERT(entry);
		}

		if (model->type == STARPU_REGRESSION_BASED || model->type == STARPU_NL_REGRESSION_BASED)
		{
			struct starpu_perfmodel_regression_model *reg_model;
			reg_model = &per_arch_model->regression;

			/* update the regression model */
			size_t job_size = _starpu_job_get_data_size(model, arch, impl, j);
			double logy, logx;
			logx = log((double)job_size);
			logy = log(measured);

			reg_model->sumlnx += logx;
			reg_model->sumlnx2 += logx*logx;
			reg_model->sumlny += logy;
			reg_model->sumlnxlny += logx*logy;
			if (reg_model->minx == 0 || job_size < reg_model->minx)
				reg_model->minx = job_size;
			if (reg_model->maxx == 0 || job_size > reg_model->maxx)
				reg_model->maxx = job_size;
			reg_model->nsample++;

			if (VALID_REGRESSION(reg_model))
			{
				unsigned n = reg_model->nsample;

				double num = (n*reg_model->sumlnxlny - reg_model->sumlnx*reg_model->sumlny);
				double denom = (n*reg_model->sumlnx2 - reg_model->sumlnx*reg_model->sumlnx);

				reg_model->beta = num/denom;
				reg_model->alpha = exp((reg_model->sumlny - reg_model->beta*reg_model->sumlnx)/n);
				reg_model->valid = 1;
			}
		}

		if (model->type == STARPU_MULTIPLE_REGRESSION_BASED)
		{
			struct starpu_perfmodel_history_entry *entry;
			struct starpu_perfmodel_history_list **list;
			list = &per_arch_model->list;

			entry = (struct starpu_perfmodel_history_entry *) calloc(1, sizeof(struct starpu_perfmodel_history_entry));
			STARPU_ASSERT(entry);

			entry->parameters = (double *) malloc(model->nparameters*sizeof(double));
			model->parameters(j->task, entry->parameters);
			entry->tag = j->task->tag_id;
			entry->duration = measured;

			struct starpu_perfmodel_history_list *link;
			link = (struct starpu_perfmodel_history_list *) malloc(sizeof(struct starpu_perfmodel_history_list));
			link->next = *list;
			link->entry = entry;
			*list = link;
		}

#ifdef STARPU_MODEL_DEBUG
		struct starpu_task *task = j->task;
		starpu_perfmodel_debugfilepath(model, arch_combs[comb], per_arch_model->debug_path, 256, impl);
		FILE *f = fopen(per_arch_model->debug_path, "a+");
		if (f == NULL)
		{
			_STARPU_DISP("Error <%s> when opening file <%s>\n", strerror(errno), per_arch_model->debug_path);
			STARPU_PTHREAD_RWLOCK_UNLOCK(&model->state->model_rwlock);
			return;
		}
		_starpu_fwrlock(f);

		if (!j->footprint_is_computed)
			(void) _starpu_compute_buffers_footprint(model, arch, impl, j);

		STARPU_ASSERT(j->footprint_is_computed);

		fprintf(f, "0x%x\t%lu\t%f\t%f\t%f\t%d\t\t", j->footprint, (unsigned long) _starpu_job_get_data_size(model, arch, impl, j), measured, task->predicted, task->predicted_transfer, cpuid);
		unsigned i;
		unsigned nbuffers = STARPU_TASK_GET_NBUFFERS(task);

		for (i = 0; i < nbuffers; i++)
		{
			starpu_data_handle_t handle = STARPU_TASK_GET_HANDLE(task, i);

			STARPU_ASSERT(handle->ops);
			STARPU_ASSERT(handle->ops->display);
			handle->ops->display(handle, f);
		}
		fprintf(f, "\n");
		_starpu_fwrunlock(f);
		fclose(f);
#endif
		STARPU_PTHREAD_RWLOCK_UNLOCK(&model->state->model_rwlock);
	}
}

void starpu_perfmodel_update_history(struct starpu_perfmodel *model, struct starpu_task *task, struct starpu_perfmodel_arch * arch, unsigned cpuid, unsigned nimpl, double measured)
{
	struct _starpu_job *job = _starpu_get_job_associated_to_task(task);

#ifdef STARPU_SIMGRID
	STARPU_ASSERT_MSG(0, "We are not supposed to update history when simulating execution");
#endif

	_starpu_init_and_load_perfmodel(model);
	/* Record measurement */
	_starpu_update_perfmodel_history(job, model, arch, cpuid, measured, nimpl);
	/* and save perfmodel on termination */
	_starpu_set_calibrate_flag(1);
}

int starpu_perfmodel_list_combs(FILE *output, struct starpu_perfmodel *model)
{
	int comb;

	fprintf(output, "Model <%s>\n", model->symbol);
	for(comb = 0; comb < model->state->ncombs; comb++)
	{
		struct starpu_perfmodel_arch *arch;
		int device;

		arch = _starpu_arch_comb_get(model->state->combs[comb]);
		fprintf(output, "\tComb %d: %d device%s\n", model->state->combs[comb], arch->ndevices, arch->ndevices>1?"s":"");
		for(device=0 ; device<arch->ndevices ; device++)
		{
			char *name = starpu_perfmodel_get_archtype_name(arch->devices[device].type);
			fprintf(output, "\t\tDevice %d: type: %s - devid: %d - ncores: %d\n", device, name, arch->devices[device].devid, arch->devices[device].ncores);
		}
	}
	return 0;
}

struct starpu_perfmodel_per_arch *starpu_perfmodel_get_model_per_arch(struct starpu_perfmodel *model, struct starpu_perfmodel_arch *arch, unsigned impl)
{
	int comb = starpu_perfmodel_arch_comb_get(arch->ndevices, arch->devices);
	if(comb == -1) return NULL;
	if(!model->state->per_arch[comb]) return NULL;

	return &model->state->per_arch[comb][impl];
}

struct starpu_perfmodel_per_arch *_starpu_perfmodel_get_model_per_devices(struct starpu_perfmodel *model, int impl, va_list varg_list)
{
	struct starpu_perfmodel_arch arch;
	va_list varg_list_copy;
	int i, arg_type;
	int is_cpu_set = 0;

	// We first count the number of devices
	arch.ndevices = 0;
	va_copy(varg_list_copy, varg_list);
	while ((arg_type = va_arg(varg_list_copy, int)) != -1)
	{
		int devid = va_arg(varg_list_copy, int);
		int ncores = va_arg(varg_list_copy, int);

		arch.ndevices ++;
		if (arg_type == STARPU_CPU_WORKER)
		{
			STARPU_ASSERT_MSG(is_cpu_set == 0, "STARPU_CPU_WORKER can only be specified once\n");
			STARPU_ASSERT_MSG(devid==0, "STARPU_CPU_WORKER must be followed by a value 0 for the device id");
			is_cpu_set = 1;
		}
		else
		{
			STARPU_ASSERT_MSG(ncores==1, "%s must be followed by a value 1 for ncores", starpu_worker_get_type_as_string(arg_type));
		}
	}
	va_end(varg_list_copy);

	// We set the devices
	arch.devices = (struct starpu_perfmodel_device*)malloc(arch.ndevices * sizeof(struct starpu_perfmodel_device));
	va_copy(varg_list_copy, varg_list);
	for(i=0 ; i<arch.ndevices ; i++)
	{
		arch.devices[i].type = va_arg(varg_list_copy, int);
		arch.devices[i].devid = va_arg(varg_list_copy, int);
		arch.devices[i].ncores = va_arg(varg_list_copy, int);
	}
	va_end(varg_list_copy);

	// Get the combination for this set of devices
	int comb = _starpu_perfmodel_create_comb_if_needed(&arch);

	free(arch.devices);

	// Realloc if necessary
	if (comb >= model->state->ncombs_set)
		_starpu_perfmodel_realloc(model, comb+1);

	// Get the per_arch object
	if (model->state->per_arch[comb] == NULL)
	{
		_starpu_perfmodel_malloc_per_arch(model, comb, STARPU_MAXIMPLEMENTATIONS);
		_starpu_perfmodel_malloc_per_arch_is_set(model, comb, STARPU_MAXIMPLEMENTATIONS);
		model->state->nimpls[comb] = 0;
	}
	model->state->per_arch_is_set[comb][impl] = 1;
	model->state->nimpls[comb] ++;

	return &model->state->per_arch[comb][impl];
}

struct starpu_perfmodel_per_arch *starpu_perfmodel_get_model_per_devices(struct starpu_perfmodel *model, int impl, ...)
{
	va_list varg_list;
	struct starpu_perfmodel_per_arch *per_arch;

	va_start(varg_list, impl);
	per_arch = _starpu_perfmodel_get_model_per_devices(model, impl, varg_list);
	va_end(varg_list);

	return per_arch;
}

int starpu_perfmodel_set_per_devices_cost_function(struct starpu_perfmodel *model, int impl, starpu_perfmodel_per_arch_cost_function func, ...)
{
	va_list varg_list;
	struct starpu_perfmodel_per_arch *per_arch;

	va_start(varg_list, func);
	per_arch = _starpu_perfmodel_get_model_per_devices(model, impl, varg_list);
	per_arch->cost_function = func;
	va_end(varg_list);

	return 0;
}

int starpu_perfmodel_set_per_devices_size_base(struct starpu_perfmodel *model, int impl, starpu_perfmodel_per_arch_size_base func, ...)
{
	va_list varg_list;
	struct starpu_perfmodel_per_arch *per_arch;

	va_start(varg_list, func);
	per_arch = _starpu_perfmodel_get_model_per_devices(model, impl, varg_list);
	per_arch->size_base = func;
	va_end(varg_list);

	return 0;
}