R. Agrawal and R. Srikant, Fast algorithms for mining association rules, Proc. 20th int. conf. very large data bases, VLDB, vol.1215, pp.487-499, 1994.

A. Ailamaki, J. David, . Dewitt, D. Mark, D. Hill et al., Dbmss on a modern processor: Where does time go, VLDB" 99, Proceedings of 25th International Conference on Very Large Data Bases, pp.266-277, 1999.

J. Arai, H. Shiokawa, T. Yamamuro, M. Onizuka, and S. Iwamura, Rabbit order: Just-in-time parallel reordering for fast graph analysis

, Parallel and Distributed Processing Symposium, pp.22-31, 2016.

A. David, J. Bader, A. Berry, D. Amos-binks, C. Chavarría-miranda et al., Stinger: Spatio-temporal interaction networks and graphs (sting) extensible representation. Georgia Institute of Technology, 2009.

J. Banerjee, W. Kim, S. Kim, and J. F. Garza, Clustering a dag for cad databases, IEEE Transactions on Software Engineering, vol.14, issue.11, pp.1684-1699, 1988.

K. Beyls, H. Erik, and . Hollander, Platform-independent cache optimization by pinpointing low-locality reuse, International Conference On Computational Science, pp.448-455, 2004.

V. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.131, issue.10, p.132, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01146070

P. Boldi, M. Rosa, M. Santini, and S. Vigna, Layered label propagation: A multiresolution coordinate-free ordering for compressing social networks, Proceedings of the 20th international conference on World wide web, pp.587-596, 2011.

H. Chafi, Z. Devito, A. Moors, T. Rompf, A. K. Sujeeth et al., Language virtualization for heterogeneous parallel computing, ACM Sigplan Notices, vol.45, pp.835-847

F. Chierichetti, R. Kumar, S. Lattanzi, M. Mitzenmacher, A. Panconesi et al., On compressing social networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.219-228, 2009.

S. Iain and . Duff, Computer solution of large sparse positive definite systems (alan george and joseph w, liu. SIAM Review, vol.26, issue.2, pp.289-291, 1984.

R. Duncan, A survey of parallel computer architectures, Computer, vol.23, issue.2, pp.5-16, 1990.

D. Ediger, R. Mccoll, E. J. Riedy, and D. A. Bader, Stinger: High performance data structure for streaming graphs, HPEC, pp.1-5, 2012.

. Stanley-c-eisenstat, M. H. Mc-gursky, A. H. Schultz, and . Sherman, Yale sparse matrix package. i. the symmetric codes, 1977.

L. Euler, Solutio problematis ad geometriam situs pertinensis, Comm. Acad

, Sci. Imper. Petropol, vol.8, pp.128-140, 1736.

. Michael-j-flynn, Some computer organizations and their effectiveness, IEEE transactions on computers, vol.100, issue.9, pp.948-960, 1972.

R. Michael, D. S. Garey, L. Johnson, and . Stockmeyer, Some simplified npcomplete graph problems, Theoretical computer science, vol.1, issue.3, pp.237-267, 1976.

Y. Joseph-e-gonzalez, H. Low, D. Gu, C. Bickson, and . Guestrin, Powergraph: Distributed graph-parallel computation on natural graphs, OSDI, vol.12, p.133, 2012.

S. Hong, H. Chafi, E. Sedlar, and K. Olukotun, Green-marl: a dsl for easy and efficient graph analysis, SIGARCH Computer Architecture News, vol.40, pp.349-362, 2012.

U. Kang and C. Faloutsos, Beyond'caveman communities': Hubs and spokes for graph compression and mining, Data Mining (ICDM), 2011 IEEE 11th International Conference on, pp.300-309, 2011.

K. Karantasis, A. Lenharth, D. Nguyen, M. Garzarán, and K. Pingali, Parallelization of reordering algorithms for bandwidth and wavefront reduction, Proceedings of the IC HPC, Networking, Storage and Analysis, pp.921-932, 2014.

G. Karsai, H. Krahn, C. Pinkernell, B. Rumpe, M. Schneider et al., Design guidelines for domain specific languages, Proceedings of the 9th OOPSLA Workshop on Domain-Specific Modeling (DSM'09), pp.7-13, 2009.

G. Karypis and V. Kumar, Multilevelk-way partitioning scheme for irregular graphs, Journal of Parallel and Distributed computing, vol.48, issue.1, pp.96-129, 1998.

L. Katz, A new status index derived from sociometric analysis. Psychometrica, vol.18, 1953.

H. Kellerer, U. Pferschy, and D. Pisinger, , 2004.

D. Lasalle and G. Karypis, Multi-threaded graph partitioning, Parallel & Distributed Processing (IPDPS), pp.225-236, 2013.

D. Liben, -. Nowell, and J. Kleinberg, The link-prediction problem for social networks, journal of the Association for Information Science and Technology, vol.58, issue.7, pp.1019-1031, 2007.

Y. Lim, C. Kang, and . Faloutsos, Slashburn: Graph compression and mining beyond caveman communities, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.12, pp.3077-3089, 2014.

A. Lumsdaine, D. Gregor, B. Hendrickson, and J. Berry, Challenges in parallel graph processing, Parallel Processing Letters, vol.17, issue.01, pp.5-20, 2007.

G. Malewicz, . Matthew-h-austern, J. C. Aart, J. C. Bik, I. Dehnert et al., Pregel: a system for large-scale graph processing, Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp.135-146, 2010.

M. Mernik, J. Heering, and A. Sloane, When and how to develop domain-specific languages, ACM computing surveys (CSUR), vol.37, issue.4, pp.316-344, 2005.

E. J. Mark and . Newman, The structure and function of complex networks, SIAM, vol.45, issue.2, pp.167-256, 2003.

D. Nguyen, A. Lenharth, and K. Pingali, A lightweight infrastructure for graph analytics, Proceedings of ACM Symposium on Operating Systems Principles, SOSP '13, pp.456-471, 2013.

N. Thomas-messi, Langage dédié pour la fouille des réseaux sociaux

L. Page, S. Brin, R. Motwani, and T. Winograd, The pagerank citation ranking: bringing order to the web, 1999.

J. Petit, Experiments on the minimum linear arrangement problem, Journal of Experimental Algorithmics (JEA), vol.8, pp.2-3, 2003.

K. Pingali, D. Nguyen, M. Kulkarni, M. Burtscher, A. Hassaan et al., The tao of parallelism in algorithms, ACM Sigplan Notices, vol.46, issue.6, pp.12-25, 2011.

J. Riedy, D. A. Bader, and H. Meyerhenke, Scalable multi-threaded community detection in social networks, vol.18, pp.1619-1628, 2012.

M. Rosenblum, E. Bugnion, S. A. Herrod, E. Witchel, and A. Gupta, The impact of architectural trends on operating system performance, SIGOPS Operating Systems Review, vol.29, issue.5, pp.285-298, 1995.

I. Safro, D. Ron, and A. Brandt, Multilevel algorithms for linear ordering problems, Journal of Experimental Algorithmics (JEA), vol.13, issue.4, p.135, 2009.

I. Safro and B. Temkin, Multiscale approach for the network compressionfriendly ordering, Journal of Discrete Algorithms, vol.9, issue.2, pp.190-202, 2011.

S. Salihoglu and J. Widom, Gps: A graph processing system, Proceedings of the 25th International Conference on Scientific and Statistical Database Management, p.22, 2013.

H. Shiokawa, Y. Fujiwara, and M. Onizuka, Fast algorithm for modularity-based graph clustering, AAAI, pp.1170-1176, 2013.

J. Shun, . Guy, and . Blelloch, Ligra: a lightweight graph processing framework for shared memory, ACM SIGPLAN Notices, vol.48, pp.135-146, 2013.

G. Jeremy, L. Siek, A. Lee, and . Lumsdaine, The Boost Graph Library: User Guide and Reference Manual, Portable Documents. Pearson Education, 2001.

J. Soman and A. Narang, Fast community detection algorithm with gpus and multicore architectures, IEEE International Parallel Distributed Processing Symposium, 2011.

F. Song, S. Moore, and J. Dongarra, Feedback-directed thread scheduling with memory considerations, 16th international symposium on HPDC, pp.97-106, 2007.

F. Song, S. Moore, and J. Dongarra, Analytical modeling for affinitybased thread scheduling on multicore platforms, Symposim on Principles and PPP, 2009.

I. Stanton and G. Kliot, Streaming graph partitioning for large distributed graphs, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1222-1230, 2012.

P. Suppa and E. Zimeo, A clustered approach for fast computation of betweenness centrality in social networks, Big Data, p.2015

, IEEE International Congress on, pp.47-54, 2015.

G. Leslie and . Valiant, A bridging model for parallel computation, Communications of the ACM, vol.33, issue.8, pp.103-111, 1990.

J. Arai, H. Shiokawa, T. Yamamuro, M. Onizuka, and S. Iwamura, Rabbit order: Just-in-time parallel reordering for fast graph analysis, Parallel and Distributed Processing Symposium, pp.22-31, 2016.

A. David, J. Bader, A. Berry, D. Amos-binks, C. Chavarría-miranda et al., Stinger: Spatio-temporal interaction networks and graphs (sting) extensible representation. Georgia Institute of Technology, 2009.

V. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, issue.10, p.10008, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01146070

D. Ediger, R. Mccoll, E. J. Riedy, and D. A. Bader, Stinger: High performance data structure for streaming graphs, HPEC, pp.1-5, 2012.

. Stanley-c-eisenstat, M. H. Mc-gursky, A. H. Schultz, and . Sherman, Yale sparse matrix package. i. the symmetric codes, 1977.

S. Hong, H. Chafi, E. Sedlar, and K. Olukotun, Green-marl: a dsl for easy and efficient graph analysis, SIGARCH Computer Architecture News, vol.40, pp.349-362, 2012.

K. Karantasis, A. Lenharth, D. Nguyen, M. Garzarán, and K. Pingali, Parallelization of reordering algorithms for bandwidth and wavefront reduction, Proceedings of the IC HPC, Networking, Storage and Analysis, pp.921-932, 2014.

G. Karypis and V. Kumar, Multilevelk-way partitioning scheme for irregular graphs, Journal of Parallel and Distributed computing, vol.48, issue.1, pp.96-129, 1998.

A. Lumsdaine, D. Gregor, B. Hendrickson, and J. Berry, Challenges in parallel graph processing, Parallel Processing Letters, vol.17, issue.01, pp.5-20, 2007.

T. M. Nguélé, M. Tchuente, and J. Méhaut, Social network ordering based on communities to reduce cache misses, Revue ARIMA, vol.24, 2015.

E. J. Mark and . Newman, The structure and function of complex networks, SIAM, vol.45, issue.2, pp.167-256, 2003.

D. Nguyen, A. Lenharth, and K. Pingali, A lightweight infrastructure for graph analytics, Proceedings of ACM Symposium on Operating Systems Principles, SOSP '13, pp.456-471, 2013.

L. Page, S. Brin, R. Motwani, and T. Winograd, The pagerank citation ranking: bringing order to the web, 1999.

J. Riedy, D. A. Bader, and H. Meyerhenke, Scalable multi-threaded community detection in social networks, vol.18, pp.1619-1628, 2012.

M. Rosenblum, E. Bugnion, S. A. Herrod, E. Witchel, and A. Gupta, The impact of architectural trends on operating system performance, SIGOPS Operating Systems Review, vol.29, issue.5, pp.285-298, 1995.

H. Shiokawa, Y. Fujiwara, and M. Onizuka, Fast algorithm for modularity-based graph clustering, AAAI, pp.1170-1176, 2013.

F. Song, S. Moore, and J. Dongarra, Feedback-directed thread scheduling with memory considerations, 16th international symposium on HPDC, pp.97-106, 2007.

F. Song, S. Moore, and J. Dongarra, Analytical modeling for affinity-based thread scheduling on multicore platforms, Symposim on Principles and PPP, 2009.

H. Wei, J. X. Yu, C. Lu, and X. Lin, Speedup graph processing by graph ordering, Proceedings of the 2016 ICMD, pp.1813-1828, 2016.

J. Yang and J. Leskovec, Defining and evaluating network communities based on ground-truth, Knowledge and Information Systems, vol.42, issue.1, pp.181-213, 2015.

J. Arai, H. Shiokawa, T. Yamamuro, M. Onizuka, and S. Iwamura, Rabbit order : Just-in-time parallel reordering for fast graph analysis, Parallel and Distributed Processing Symposium, pp.22-31, 2016.

A. David, J. Bader, A. Berry, D. Amos-binks, C. Chavarría-miranda et al., Stinger : Spatio-temporal interaction networks and graphs (sting) extensible representation. Georgia Institute of Technology, 2009.

D. Vincent, J. Blondel, R. Guillaume, E. Lambiotte, and . Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics : Theory and Experiment, issue.10, p.10008, 2008.

Q. Duong, S. Goel, J. Hofman, and S. Vassilvitskii, Sharding social networks, Proceedings of the sixth ACM international conference on Web search and data mining, pp.223-232, 2013.

D. Ediger, R. Mccoll, E. J. Riedy, and D. A. Bader, Stinger : High performance data structure for streaming graphs, HPEC, pp.1-5, 2012.

. Stanley-c-eisenstat, M. H. Mc-gursky, A. H. Schultz, and . Sherman, Yale sparse matrix package. i. the symmetric codes, 1977.

R. Michael, D. S. Garey, L. Johnson, and . Stockmeyer, Some simplified np-complete graph problems, Theoretical computer science, vol.1, issue.3, pp.237-267, 1976.

S. Hong, H. Chafi, E. Sedlar, and K. Olukotun, Green-marl : a dsl for easy and efficient graph analysis, In ACM SIGARCH Computer Architecture News, vol.40, pp.349-362, 2012.

I. Hoque and I. Gupta, Social network-aware disk management, 2010.

L. Katz, A new status index derived from sociometric analysis. Psychometrica, vol.18, 1953.

Z. Li, Z. Chen, and Y. Zhou, Mining block correlations to improve storage performance, ACM Transactions on Storage (TOS), vol.1, issue.2, pp.213-245, 2005.

T. Messi-nguélé, M. Tchuenté, and J. Mehaut, Exploitation de la structure en communautés pour la réduction de défauts de cache dans la fouille des réseaux sociaux, Conférence de Recherche en Informatique (CRI), 2015.

B. Ngonmang, M. Tchuente, and E. Viennet, Local community identification in social networks, Parallel Processing Letters, vol.22, issue.01, p.1240004, 2012.

D. Nguyen, A. Lenharth, and K. Pingali, A lightweight infrastructure for graph analytics, Proceedings of ACM Symposium on Operating Systems Principles, SOSP '13, pp.456-471, 2013.

L. Page, S. Brin, R. Motwani, and T. Winograd, The pagerank citation ranking : bringing order to the web, 1999.

L. Polok, V. Ila, and P. Smrz, Cache efficient implementation for block matrix operations, Proceedings of the High Performance Computing Symposium

, ARIMA, 2017 ARIMA Journal Computer Simulation International, vol.24, 2013.

J. Riedy, D. A. Bader, and H. Meyerhenke, Scalable multi-threaded community detection in social networks, IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum, vol.18, pp.1619-1628, 2012.

R. Shahnaz and A. Usman, Blocked-based sparse matrix-vector multiplication on distributed memory parallel computers, Int. Arab J. Inf. Technol, vol.8, issue.2, pp.130-136, 2011.

J. Yang and J. Leskovec, Defining and evaluating network communities based on groundtruth, Numérotation des graphes sociaux, vol.42, p.47, 2015.

E. Sociaux, Représentation de (G) tenant compte des communautés 2.2.2. Cas des processeurs laissant la gestion du cache au programmeur C'est le cas par exemple du processeur MPPA de Kalray o, vol.2

L. Ici and . Survient-un-défaut-de-cache, Cette communauté est alors chargée dans le cache en suivant l'un des algorithmes classiques: ? algorithme optimal (la ligne de cache qui ne sera pas utilisée pour la plus grande période de temps est remplacée), ? algorithme aléatoire, ? LRU Least Recently Used,-FIFO First In First Out, ? LFU Least Frequently Used

, Dans le cas o` u elle est grande, on pourra se contenter de la communauté locale (ou d'un autre moyen permettant de réduire cette taille). Dans le cas o` u elle très petite, on chargera aussi la communauté ayant le plus de liens avec la communauté initiale. (On se retrouve ici avec unprobì eme du sacà sac, Ici, il peut arriver que la taille de la communauté soit très grande ou très petite

, Ces algorithmes sont ensuite utilisés pour trouver la corrélation entre les blocs d'une mémoire: les blocs sont perçus comme des items, les r` egles d'association issues de ces items permettent de faire du prefetching. Dans notre cas, nous nous servons de la détection des communautés. Plusieurs travaux visent l'usage d'une meilleur représentation des matrices creuses pour accroitre les performances de certaines applications (dans la résolution des systèmes d'´ equations linéaires):-Lukas Polok et co-auteurs [9] proposent une structure de données basée sur la représentation en sous-blocs d'une matrice creuse. Cette représentation permet de réduire les défauts de cache lors des opérations arithmétiques effectuées sur la matrice pendant l'exécution.-Rukhsana S, Nous agissons plutôt sur la mémoire vive (tandis qu'ils agissent sur le disque dur)

, Les auteurs montrent que, non seulement cette représentation permet d'´ economiser plus d'espace, mais aussi permet d'obtenir une meilleur perfomance (lors de la multiplication matrice-vecteur)

C. Dans-notre, nous recherchons la structure de données la plus appropriée pour les programmes de la fouille des réseaux sociaux

E. Sociaux-´, , vol.10, p.458

, L1-dcache-load-misses(millions) 63, vol.802, p.528

, Table 2: Nombre de défauts de cache

S. Hong, H. Chafi, E. Sedlar, and K. Olukotun, Green-marl: a dsl for easy and efficient graph analysis, In ACM SIGARCH Computer Architecture News, vol.40, pp.349-362, 2012.

I. Hoque and I. Gupta, Social network-aware disk management, 2010.

L. Katz, A new status index derived from sociometric analysis. Psychometrica, vol.18, 1953.

Z. Li, Z. Chen, and Y. Zhou, Mining block correlations to improve storage performance, ACM Transactions on Storage (TOS), vol.1, issue.2, pp.213-245, 2005.

D. Nguyen, A. Lenharth, and K. Pingali, A lightweight infrastructure for graph analytics, Proceedings of ACM Symposium on Operating Systems Principles, SOSP '13, pp.456-471, 2013.

L. Polok, V. Ila, and P. Smrz, Cache efficient implementation for block matrix operations, Proceedings of the High Performance Computing Symposium, 2013.

J. Riedy, D. A. Bader, and H. Meyerhenke, Scalable multi-threaded community detection in social networks, IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum, vol.18, pp.1619-1628, 2012.

R. Shahnaz and A. Usman, Blocked-based sparse matrix-vector multiplication on distributed memory parallel computers, Int. Arab J. Inf. Technol, vol.8, issue.2, pp.130-136, 2011.