A. , Partage automatique des blocs suivant les SM

, Il existe plusieurs types de mémoire sur une carte graphique

?. La-mémoire-constante, qui est celle où les variables constantes et les arguments du kernel sont enregistrés. Elle est assez lente mais elle dispose d'un cache de 8 kb

?. La-mémoire-globale, qui est accessible par tous les blocs et leurs threads. Son temps de lecture et d'écriture pouvant être assez long, le temps gagné par l'accélération des calculs peut être perdu si l'on effectue un nombre élevé d'opérations sur cette mémoire

?. La-mémoire-partagée, dont le temps de latence est très faible (jusqu'à 100 fois moindre que le temps de latence de la mémoire globale)

, ? Les registres, qui constituent les mémoires les plus rapides mais qui sont dédiés à chaque thread

, ? La mémoire locale, qui est plus lente que la mémoire partagée et qui est utilisée pour toutes les opérations qui dépassent le cadre des registres

C. Figure, 4 -Hiérarchie de la mémoire GPU

J. F. Adamowski, «Peak daily water demand forecast modeling using artificial neural networks», Journal of Water Resources Planning and Management, vol.134, issue.2, pp.119-128, 2008.

A. Agresti and M. Kateri, Categorical data analysis, p.33, 2011.

S. Ahmad, A. Lavin, S. Purdy, and Z. A. , «Unsupervised real-time anomaly detection for streaming data, Neurocomputing, vol.262, pp.134-147, 2017.

H. Akaike, A new look at the statistical model identification», dans Selected Papers of Hirotugu Akaike, vol.17, pp.215-222, 1974.

K. Aksela and M. A. , «Demand estimation with automated meter reading in a distribution network, Journal of Water Resources Planning and Management, vol.137, issue.5, pp.456-467, 2010.

A. Altunkaynak and T. A. Nigussie, «Monthly water consumption prediction using season algorithm and wavelet transform-based models, Journal of Water Resources Planning and Management, vol.143, issue.6, p.35, 2017.

C. Ambroise and G. Govaert, «Clustering by maximizing a fuzzy classification maximum likelihood criterion, vol.17, pp.187-192, 2000.

P. G. Arabie and . De-soete, Clustering and classification, p.14, 1996.

A. Aue and L. Horváth, «Structural breaks in time series, Journal of Time Series Analysis, vol.34, issue.1, pp.1-16, 2013.

M. Basseville, «Detecting changes in signals and systems-a survey, Automatica, vol.24, issue.3, pp.309-326, 1988.

M. Basseville and I. V. Nikiforov, Detection of abrupt changes : theory and application, vol.104, p.70, 1993.
URL : https://hal.archives-ouvertes.fr/hal-00008518

Y. Bengio, «Markovian models for sequential data, Neural computing surveys, vol.2, pp.129-162, 0199.

Y. Bengio and P. Frasconi, «Input-output hmms for sequence processing, IEEE Transactions on Neural Networks, vol.7, issue.5, pp.1231-1249, 1996.

C. Biernacki, G. Celeux, and G. Govaert, «Assessing a mixture model for clustering with the integrated completed likelihood, IEEE transactions on pattern analysis and machine intelligence, vol.22, p.40, 2000.

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the em algorithm for getting the highest likelihood in multivariate gaussian mixture models, Computational Statistics & Data Analysis, vol.41, pp.561-575, 2003.

J. Bougadis, K. Adamowski, and R. Diduch, Short-term municipal water demand forecasting, vol.19, pp.137-148, 2005.

H. Bozdogan, «Model selection and akaike's information criterion (aic) : The general theory and its analytical extensions, Psychometrika, vol.52, issue.3, pp.345-370, 1987.

T. Britton, G. Cole, R. Stewart, and D. Wiskar, Remote diagnosis of leakage in residential households», vol.35, pp.89-93, 2008.

A. Candelieri and F. A. , «Identifying typical urban water demand patterns for a reliable short-term forecasting-the icewater project approach, Procedia Engineering, vol.89, pp.1004-1012, 2014.

Y. Cao, Y. Li, S. Coleman, A. Belatreche, and T. M. Mcginnity, «Adaptive hidden markov model with anomaly states for price manipulation detection, IEEE transactions on neural networks and learning systems, vol.26, pp.318-330, 2015.

R. Cardell-oliver, «Water use signature patterns for analyzing household consumption using medium resolution meter data, Water Resources Research, vol.49, pp.8589-8599, 2013.

F. Caron, A. Doucet, and R. Gottardo, On-line changepoint detection and parameter estimation with application to genomic data, Statistics and Computing, vol.22, issue.2, pp.579-595, 2012.
URL : https://hal.archives-ouvertes.fr/inria-00577217

S. M. Cavanagh, W. M. Hanemann, and R. N. Stavins, Muffled price signals : household water demand under increasing-block prices, 2002.

G. Celeux and G. Govaert, «A classification em algorithm for clustering and two stochastic versions, Computational statistics & Data analysis, vol.14, p.38, 1992.

G. Celeux and G. Soromenho, «An entropy criterion for assessing the number of clusters in a mixture model, Journal of classification, vol.13, issue.2, pp.195-212, 1996.

N. Cheifetz, Détection et classification de signatures temporelles CAN pour l'aide à la maintenance de sous-systèmes d'un véhicule de transport collectif, p.77, 2013.

N. Cheifetz, Z. Noumir, A. Samé, A. Sandraz, C. Féliers et al., «Modeling and clustering water demand patterns from real-world smart meter data, Drinking Water Engineering and Science, vol.10, issue.2, pp.75-82, 2017.

N. Cheifetz, A. Same, P. Aknin, E. De, D. Verdalle et al., «A sequential testing procedure for multiple change-point detection in a stream of pneumatic door signatures, dans 2013 12th International Conference on Machine Learning and Applications, vol.1, pp.117-122, 2013.

S. Chen and P. Gopalakrishnan, «Speaker, environment and channel change detection and clustering via the bayesian information criterion, dans Proc. DARPA broadcast news transcription and understanding workshop, vol.8, p.72, 1998.

X. C. Chen, K. Steinhaeuser, S. Boriah, S. Chatterjee, and V. Kumar, Contextual time series change detection», dans Proceedings of the 2013 SIAM International Conference on Data Mining, pp.503-511, 2013.

H. Cho, «Change-point detection in panel data via double cusum statistic, Electronic Journal of Statistics, vol.10, issue.2, pp.2000-2038, 2016.

S. Cho, A. S. Cohen, and B. Bottge, «Detecting intervention effects using a multilevel latent transition analysis with a mixture irt model, Psychometrika, vol.78, issue.3, pp.576-600, 2013.

G. Cole and R. A. Stewart, «Smart meter enabled disaggregation of urban peak water demand : precursor to effective urban water planning, Urban Water Journal, vol.10, issue.3, pp.174-194, 2013.

L. M. Collins and S. T. Lanza, Latent class and latent transition analysis : With applications in the social, behavioral, and health sciences, vol.718, p.96, 2009.

C. Cuda, Best practices guide», Nvidia Corporation, vol.107, p.109, 2012.

A. Cutler and M. P. Windham, «Information-based validity functionals for mixture analysis», dans Proceedings of the first US/Japan Conference on the Frontiers of statistical modeling : An informational approach, vol.17, pp.149-170, 1994.

H. Day and K. C. , «Rule 1 : No watts no water, dans Proceedings of 24th Annual WasteResue Symposium : Where Has All the Water Gone, vol.2, 2009.

A. P. Dempster, N. M. Laird, and D. B. Rubin, «Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society : Series B (Methodological), vol.39, issue.1, p.38, 1977.

E. Domene and D. Saurí, Urbanisation and water consumption : Influencing factors in the metropolitan region of barcelona, Urban Studies, vol.43, issue.9, pp.1605-1623, 2006.

R. E. Edwards, J. New, and L. E. Parker, Predicting future hourly residential electrical consumption : A machine learning case study, Energy and Buildings, vol.49, pp.591-603, 2012.

M. Ester, H. Kriegel, J. Sander, and X. Xu, «A density-based algorithm for discovering clusters in large spatial databases with noise, », dans Kdd, vol.96, pp.226-231, 1996.

B. Everett, An introduction to latent variable models, p.32, 2013.

P. Fearnhead and Z. Liu, On-line inference for multiple changepoint problems, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.69, issue.4, pp.589-605, 2007.

K. S. Fielding, A. Spinks, S. Russell, R. Mccrea, R. Stewart et al., «An experimental test of voluntary strategies to promote urban water demand management, Journal of environmental management, vol.114, issue.5, pp.343-351, 2013.

C. Flath, D. Nicolay, T. Conte, C. Van-dinther, and L. Filipova-neumann, Cluster analysis of smart metering data, vol.4, pp.31-39, 2012.

K. Fokianos, E. Gombay, and A. Hussein, «Retrospective change detection for binary time series models, Journal of Statistical Planning and Inference, vol.145, pp.102-112, 2014.

K. Fokianos and B. K. , «Prediction and classification of non-stationary categorical time series, Journal of multivariate analysis, vol.67, issue.2, pp.277-296, 1998.

C. Fraley and A. E. Raftery, Model-based clustering, discriminant analysis, and density estimation», Journal of the American statistical Association, vol.97, pp.611-631, 2002.

J. Froehlich, E. Larson, S. Gupta, G. Cohn, M. Reynolds et al., «Disaggregated end-use energy sensing for the smart grid, IEEE Pervasive Computing, vol.10, issue.1, pp.28-39, 2010.

S. Gaffney and P. Smyth, Trajectory clustering with mixtures of regression models», dans KDD, vol.99, pp.63-72, 1999.

F. Gagliardi, S. Alvisi, Z. Kapelan, and M. Franchini, «A probabilistic short-term water demand forecasting model based on the markov chain, vol.9, p.36, 2017.

J. L. Gauvain, «Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains, IEEE transactions on speech and audio processing, vol.2, pp.291-298, 1994.

D. Gay, R. Guigourès, M. Boullé, and F. Clérot, Cats & co : Categorical time series coclustering», 2015.

E. Gombay, F. Li, and H. Y. , «Retrospective change detection in categorical time series, Communications in Statistics-Theory and Methods, vol.46, p.80, 2017.

G. Govaert and M. Nadif, «Clustering with block mixture models, Pattern Recognition, vol.36, issue.2, pp.463-473, 2003.

P. J. Green, «Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives», Journal of the Royal Statistical Society : Series B (Methodological), vol.46, issue.2, pp.149-170, 1984.

F. Gustafsson, Adaptive filtering and change detection, vol.1, p.68, 2000.

D. S. Gutzler and J. S. Nims, «Interannual variability of water demand and summer climate in albuquerque, new mexico», Journal of Applied Meteorology, vol.44, pp.1777-1787, 2005.

S. Haben, C. Singleton, and P. Grindrod, Analysis and clustering of residential customers energy behavioral demand using smart meter data, IEEE transactions on smart grid, vol.7, pp.136-144, 2015.

S. H. Hanke and L. D. Mare, «Residential water demand : A pooled, time series, cross section study of malmö, sweden 1», JAWRA Journal of the American Water Resources Association, vol.18, issue.4, pp.621-626, 1982.

P. Harrington, Machine learning in action, 2012.

M. Höhle, «Online change-point detection in categorical time series», dans Statistical modelling and regression structures, vol.73, pp.377-397, 2010.

P. W. Holland and R. E. Welsch, «Robust regression using iteratively reweighted leastsquares», Communications in Statistics-theory and Methods, vol.6, issue.9, p.99, 1977.

L. House-peters, B. Pratt, and H. C. , «Effects of urban spatial structure, sociodemographics, and climate on residential water consumption in hillsboro, JAWRA Journal of the American Water Resources Association, vol.46, issue.1, pp.461-472, 2010.

L. A. House-peters and H. C. , Urban water demand modeling : Review of concepts, methods, and organizing principles, Water Resources Research, vol.47, issue.7, 2011.

L. Hubert and P. A. , «Comparing partitions, Journal of classification, vol.2, issue.1, pp.193-218, 1985.

O. Hunaidi, A. Wang, M. Bracken, T. Gambino, and C. Fricke, «Detecting leaks in water distribution pipes, Arab Water World, vol.29, issue.4, pp.52-55, 2005.

R. Isermann, Process fault detection based on modeling and estimation methods-a survey», automatica, vol.20, pp.387-404, 1984.

A. Jain, A. K. Varshney, and U. C. Joshi, Short-term water demand forecast modelling at iit kanpur using artificial neural networks», Water resources management, vol.15, pp.299-321, 2001.

C. V. Jones and J. R. Morris, «Instrumental price estimates and residential water demand, Water Resources Research, vol.20, issue.2, pp.197-202, 1984.

J. Jung, V. Paxson, A. W. Berger, and H. Balakrishnan, «Fast portscan detection using sequential hypothesis testing, dans IEEE Symposium on Security and Privacy, vol.74, pp.211-225, 2004.

R. E. Kass and A. E. Raftery, Bayes factors, vol.90, pp.773-795, 1995.

D. S. Kenney, C. Goemans, R. Klein, J. Lowrey, and K. R. , «Residential water demand management : lessons from aurora, JAWRA Journal of the American Water Resources Association, vol.44, issue.1, pp.192-207, 2008.

E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, «Dimensionality reduction for fast similarity search in large time series databases, Knowledge and information Systems, vol.3, issue.3, pp.263-286, 2001.

U. Kumar and V. Jain, «Time series models (grey-markov, grey model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in india, vol.35, pp.1709-1716, 2010.

J. Kwac, J. Flora, and R. R. , «Household energy consumption segmentation using hourly data, IEEE Transactions on Smart Grid, vol.5, issue.1, pp.420-430, 2014.

W. Labeeuw and G. Deconinck, «Residential electrical load model based on mixture model clustering and markov models, IEEE Transactions on Industrial Informatics, vol.9, issue.3, pp.1561-1569, 2013.

S. T. Lanza and L. M. Collins, «A new sas procedure for latent transition analysis : Transitions in dating and sexual risk behavior.», Developmental psychology, vol.44, p.33, 2008.

P. F. Lazarsfeld and N. W. Henry, Latent structure analysis, p.32, 1968.

F. Li, A. Cohen, B. Bottge, and J. Templin, «A latent transition analysis model for assessing change in cognitive skills, Educational and Psychological Measurement, vol.76, issue.2, pp.181-204, 2016.

J. Li, F. Tsung, and C. Zou, «Directional change-point detection for process control with multivariate categorical data, Naval Research Logistics (NRL), vol.60, pp.160-173, 2013.

J. Lin, E. Keogh, S. Lonardi, and B. Chiu, «A symbolic representation of time series, with implications for streaming algorithms, dans Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, vol.8, p.96, 2003.

G. Lorden, «Procedures for reacting to a change in distribution, The Annals of Mathematical Statistics, vol.42, issue.6, p.71, 1971.

T. M. Luong, V. Perduca, and G. Nuel, Hidden markov model applications in change-point analysis», 2012.

J. Macqueen, «Some methods for classification and analysis of multivariate observations, dans Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol.1, pp.281-297, 1967.

R. A. Martin, W. F. Velicer, and J. L. Fava, «Latent transition analysis to the stages of change for smoking cessation, Addictive Behaviors, vol.21, issue.1, pp.67-80, 1996.

P. W. Mayer, W. B. Deoreo, E. M. Opitz, J. C. Kiefer, W. Y. Davis et al., Residential end uses of water, vol.5, p.7, 1999.

A. L. Mccutcheon, Latent class analysis, vol.64, 1987.

T. Mcintyre, Data retention in ireland : Privacy, policy and proportionality, vol.24, pp.326-334, 2008.

S. Mckenna, F. Fusco, and B. E. , «Water demand pattern classification from smart meter data, Procedia Engineering, vol.70, pp.1121-1130, 2014.

G. Mclachlan and T. Krishnan, The EM algorithm and extensions, vol.382, p.15, 2007.

G. J. Mclachlan and K. E. Basford, Mixture models : Inference and applications to clustering, vol.84, p.14, 1988.

F. Melzi, A. Samé, M. Zayani, and L. Oukhellou, «A dedicated mixture model for clustering smart meter data : identification and analysis of electricity consumption behaviors, vol.10, p.33, 2017.

G. V. Moustakides, «Optimal stopping times for detecting changes in distributions, The Annals of Statistics, vol.14, issue.4, pp.1379-1387, 1986.

M. Nadif and G. Govaert, «Block clustering of contingency table and mixture model, dans International Symposium on Intelligent Data Analysis, vol.96, pp.249-259, 2005.

C. Nvidia, Nvidia cuda c programming guide, Nvidia Corporation, vol.120, pp.8-107, 2011.

. Oracle, «Smart metering for water utilities, 2009.

E. S. Page, Continuous inspection schemes, vol.41, pp.100-115, 1954.

L. R. Rabiner, «A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, vol.77, pp.257-286, 1989.

K. Roos, T. Terlaky, and J. Vial, Theory and algorithms for linear optimization -an interior point approach», dans Wiley-Interscience series in discrete mathematics and optimization, vol.39, p.99, 1998.

I. H. Rowlands, T. Reid, and P. P. , Research with disaggregated electricity end-use data in households : review and recommendations, vol.4, pp.383-396, 2015.

A. Samé, C. Ambroise, and G. Govaert, «An online classification em algorithm based on the mixture model, Statistics and Computing, vol.17, issue.3, p.96, 2007.

A. Samé and G. Govaert, Online time series segmentation using temporal mixture models and bayesian model selection, dans 2012 11th International Conference on Machine Learning and Applications, vol.1, pp.602-605, 2012.

A. Samé, Z. Noumir, N. Cheifetz, A. Sandraz, and C. Féliers, «Décomposition et classification de données fonctionnelles pour l'analyse de la consommation, vol.21, p.22, 2016.

M. Sato and S. Ishii, Neural computation, vol.12, issue.2, pp.407-432, 2000.

G. Schwarz, «Estimating the dimension of a model, The annals of statistics, vol.6, pp.461-464, 1978.

G. Schwarz, «Estimating the dimension of a model, The annals of statistics, vol.6, pp.461-464, 1978.

J. S. Simonoff, Smoothing methods in statistics, vol.88, 2012.

P. Smyth, «Probabilistic model-based clustering of multivariate and sequential data, dans Proceedings of the Seventh International Workshop on AI and Statistics, vol.32, pp.299-304, 1999.

G. Soromenho, «Comparing approaches for testing the number of components in a finite mixture model, Computational Statistics, vol.9, issue.1, pp.65-78, 1994.

R. A. Stewart, R. Willis, D. Giurco, K. Panuwatwanich, and G. Capati, «Web-based knowledge management system : linking smart metering to the future of urban water planning, Australian Planner, vol.47, issue.2, pp.66-74, 2010.

J. F. Thomas and G. J. Syme, «Estimating residential price elasticity of demand for water : A contingent valuation approach, Water Resources Research, vol.24, issue.11, pp.1847-1857, 1988.

W. F. Velicer, R. A. Martin, and L. M. Collins, «Latent transition analysis for longitudinal data, Addiction, vol.91, pp.197-210, 1996.

D. Walker, E. Creaco, L. Vamvakeridou-lyroudia, R. Farmani, Z. Kapelan et al., Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks», Procedia Engineering, vol.119, pp.1419-1428, 2015.

T. Wang, G. Lu, J. Liu, and P. Yan, Adaptive change detection for long-term machinery monitoring using incremental sliding-window, Chinese Journal of Mechanical Engineering, vol.30, issue.6, pp.1338-1346, 2017.

Y. Wang, Q. Chen, C. Kang, and Q. Xia, Clustering of electricity consumption behavior dynamics toward big data applications», IEEE transactions on smart grid, vol.7, p.34, 2016.

J. H. Ward-jr, «Hierarchical grouping to optimize an objective function, Journal of the American statistical association, vol.58, pp.236-244, 1963.

P. J. Werbos, «Computational intelligence for the smart grid-history, challenges, and opportunities, IEEE Computational Intelligence Magazine, vol.6, issue.3, pp.14-21, 2011.

R. M. Willis, R. A. Stewart, K. Panuwatwanich, P. R. Williams, and A. L. Hollingsworth, Quantifying the influence of environmental and water conservation attitudes on household end use water consumption», Journal of environmental management, vol.92, issue.8, pp.1996-2009, 2011.

A. Willsky and H. Jones, «A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems, IEEE Transactions on Automatic control, vol.21, issue.1, pp.108-112, 1976.

. Bibliographie,

S. Zhou, T. Mcmahon, A. Walton, and J. L. , Forecasting operational demand for an urban water supply zone, Journal of hydrology, vol.259, pp.189-202, 2002.

S. L. Zhou, T. A. Mcmahon, A. Walton, and J. L. , «Forecasting daily urban water demand : a case study of melbourne, Journal of hydrology, vol.236, pp.153-164, 2000.

A. Liste-des-publications-revues-?-milad-leyli-abadi, L. Samé, N. Oukhellou, P. Cheifetz, C. Mandel et al., Mixture of Joint Nonhomogeneous Markov Chains to Cluster and Model Water Consumption Behavior Sequences, 2019.

A. ?-milad-leyli-abadi, L. Samé, and . Oukhellou, Nicolas Cheifetz et Pierre Mandel. « Online change point detection for categorical time series using an adaptive threshold ». En soumission dans « Neurocomputing, CONFÉRENCES INTERNATIONALES

M. ?-allou-samé, L. Leyli-abadi, and . Oukhellou, « Change detection in smart grids using dynamic mixtures of t-distributions, Congress on Condition Monitoring (WCCM), 2019.

M. ?-allou-samé and . Leyli-abadi, « Change Point Detection in Periodic Panel Data Using a Mixture-Model-Based Approach, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2019.

A. ?-milad-leyli-abadi, L. Samé, N. Oukhellou, P. Cheifetz, and . Mandel, Cédric Féliers, et Olivier Chesneau. « Mixture of Non-homogeneous Hidden Markov Models for Clustering and Prediction of Water Consumption Time Series, International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2018.

A. ?-milad-leyli-abadi, L. Samé, N. Oukhellou, P. Cheifetz, and . Mandel, Cédric Feliers, et Olivier Chesneau. « Predictive classification of water consumption time series using non-homogeneous markov models, IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017.

L. ?-milad-leyli-abadi and . Labiod, et Mohamed Nadif. « Denoising autoencoder as an effective dimensionality reduction and clustering of text data, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2017.

C. Nationale,

A. ?-milad-leyli-abadi, L. Samé, and . Oukhellou, « Détection en ligne de multiples changements dans un panel de données catégorielles, 2019.

A. ?-milad-leyli-abadi, L. Samé, and . Oukhellou, « Mélange de chaînes de Markov nonhomogènes pour la classification et la prévision des habitudes de consommation issues d'un réseau d'eau intelligent, 2018.