A. Abh-+-13]-guillaume-aupy, T. Benoit, Y. Hérault, F. Robert, D. Vivien et al., On the combination of silent error detection and checkpointing, IEEE 19th Pacific Rim International Symposium on Dependable Computing, PRDC 2013, pp.11-20, 2013.

G. Aew-+-11]-hasan-abbasi, M. Eisenhauer, K. Wolf, S. Schwan, and . Klasky, Just in Time: Adding Value to the IO Pipelines of High Performance Applications with JITStaging, International symposium on High performance distributed computing, pp.27-36, 2011.

G. , The validity of the single processor approach to achieving large scale computing capabilities, AFIPS Conference Proceedings, pp.483-485, 1967.

G. Aupy, Y. Robert, F. Vivien, and D. Zaidouni, Checkpointing algorithms and fault prediction, J. Parallel Distrib. Comput, vol.74, issue.2, pp.2048-2064, 2014.
DOI : 10.1016/j.jpdc.2013.10.010

URL : https://hal.archives-ouvertes.fr/hal-00908446

C. Augonnet, S. Thibault, R. Namyst, and P. Wacrenier, StarPU: a unified platform for task scheduling on heterogeneous multicore architectures, Concurrency and Computation: Practice and Experience, vol.23, issue.2, pp.187-198, 2011.
DOI : 10.1007/978-3-642-03869-3_80

URL : https://hal.archives-ouvertes.fr/inria-00384363

M. +-09]-hasan-abbasi, G. Wolf, S. Eisenhauer, K. Klasky, F. Schwan et al., DataStager: Scalable Data Staging Services for Petascale Applications, 18th ACM international symposium on High performance distributed computing, pp.39-48, 2009.

B. Aww-+-16]-utkarsh-ayachit, M. Whitlock, B. Wolf, B. Loring, D. Geveci et al., The sensei generic in situ interface, Proceedings of the 2Nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization, ISAV '16, pp.40-44, 2016.

C. Janine, H. Bennett, . Abbasi, . Peer-timo, R. Bremer et al., Combining in-situ and in-transit processing to enable extreme-scale scientific analysis, Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, p.49, 2012.

+. Biddiscombe, J. Soumagne, G. Oger, D. Guibert, and J. Piccinali, Parallel Computational Steering and Analysis for HPC Applications using a ParaView Interface and the HDF5 DSM Virtual File Driver, Eurographics Symposium on Parallel Graphics and Visualization, pp.91-100, 2011.
DOI : 10.1109/tvcg.2012.63

URL : https://hal.archives-ouvertes.fr/hal-00651814

M. Bauer, S. Treichler, E. Slaughter, and A. Aiken, Legion: Expressing locality and independence with logical regions, Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC '12, vol.66, pp.1-66, 2012.
DOI : 10.1109/sc.2012.71

URL : http://theory.stanford.edu/%7Eaiken/publications/papers/sc12.pdf

J. Capul, S. Morais, and J. Lekien, Padawan: A python infrastructure for loosely coupled in situ workflows, Proceedings of the Workshop on In Situ Infrastructures for Enabling ExtremeScale Analysis and Visualization, ISAV '18, pp.7-12, 2018.

M. Dorier, G. Antoniu, F. Cappello, M. Snir, and L. Orf, Damaris: How to Efficiently Leverage Multicore Parallelism to Achieve Scalable, Jitter-free I/O, CLUSTER -IEEE International Conference on Cluster Computing, 2012.
DOI : 10.1109/cluster.2012.26

URL : https://hal.archives-ouvertes.fr/hal-00715252

E. Dirand, L. Colombet, and B. Raffin, TINS: A TaskBased Dynamic Helper Core Strategy for In Situ Analytics, SCA18 -Supercomputing Frontiers Asia, 2018.
DOI : 10.1007/978-3-319-69953-0_10

URL : https://hal.archives-ouvertes.fr/hal-01730910

M. Dreher and T. Peterka, Bredala: Semantic Data Redistribution for In Situ Applications, Proceedings of IEEE Cluster, 2016.
DOI : 10.1109/cluster.2016.30

URL : https://hal.archives-ouvertes.fr/hal-01358482

C. Docan, M. Parashar, and S. Klasky, Dart: a substrate for high speed asynchronous data io, 17th international symposium on High performance distributed computing, pp.219-220, 2008.
DOI : 10.1002/cpe.1567

URL : http://nsfcac.rutgers.edu/TASSL/Papers/dart_hpdc.pdf

C. Docan, M. Parashar, and S. Klasky, DataSpaces: an Interaction and Coordination Framework for Coupled Simulation Workflows, vol.15, pp.163-181, 2012.
DOI : 10.1007/s10586-011-0162-y

M. Dreher and B. Raffin, A flexible framework for asynchronous in situ and in transit analytics for scientific simulations, p.14, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00941413

, IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp.277-286, 2014.

M. Dorier, R. Sisneros, T. Peterka, G. Antoniu, and D. Semeraro, Damaris/Viz: a Nonintrusive, Adaptable and UserFriendly In Situ Visualization Framework, IEEE Symposium on Large Data Analysis and Visualization (LDAV), 2013.
DOI : 10.1109/ldav.2013.6675160

URL : https://hal.archives-ouvertes.fr/hal-00859603

E. Deelman, G. Singh, M. Su, J. Blythe, Y. Gil et al., Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems, DSS + 05, vol.13, pp.219-237, 2005.
DOI : 10.1155/2005/128026

URL : http://downloads.hindawi.com/journals/sp/2005/128026.pdf

N. Fabian, K. Moreland, D. Thompson, A. C. Bauer, P. Marion et al., The paraview coprocessing library: A scalable, general purpose in situ visualization library, 2011 IEEE Symposium on Large Data Analysis and Visualization, pp.89-96, 2011.
DOI : 10.1109/ldav.2011.6092322

R. Hoque, T. Herault, G. Bosilca, and J. Dongarra, Dynamic Task Discovery in PaRSEC: A Data-flow Task-based Runtime, Proceedings of the 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, ScalA '17, vol.6, pp.1-6, 2017.
DOI : 10.1145/3148226.3148233

T. Harris, M. Maas, and V. J. Marathe, Callisto: Coscheduling parallel runtime systems, Proceedings of the Ninth European Conference on Computer Systems (EuroSys'14, vol.24, pp.1-24, 2014.

M. Herlihy and N. Shavit, The art of multiprocessor programming, 2011.
DOI : 10.1145/1146381.1146382

]. A. +-17, E. Heirich, M. Slaughter, W. Papadakis, T. Lee et al., In situ visualization with task-based parallelism, Workshop on In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization, p.17, 2017.

. Khal-+-14]-hartmut, T. Kaiser, B. Heller, A. Adelstein-lelbach, D. Serio et al., Hpx: A task based programming model in a global address space, Proceedings of the 8th International Conference on Partitioned Global Address Space Programming Models, 2014.

J. Laa-+-17]-matthew-larsen, U. Ahrens, E. Ayachit, H. Brugger, B. Childs et al., The alpine in situ infrastructure: Ascending from the ashes of strawman, Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization, pp.42-46, 2017.

I. Bertram-ludäscher, C. Altintas, D. Berkley, E. Higgins, M. Jaeger et al., Scientific Workflow Management and the Kepler System: Research Articles, Concurr. Comput. : Pract. Exper, vol.18, issue.10, pp.1039-1065, 2006.

M. Larsen, C. Harrison, J. Kress, D. Pugmire, J. S. Meredith et al., Performance Modeling of In Situ Rendering, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC16), vol.24, pp.1-24, 2016.
DOI : 10.1109/sc.2016.23

URL : https://scholarsbank.uoregon.edu/xmlui/bitstream/1794/22297/1/Larsen_oregon_0171A_11767.pdf

J. F. Lofstead, S. Klasky, K. Schwan, N. Podhorszki, and C. Jin, Flexible io and integration for scientific codes through the adaptable io system (adios), Proceedings of the 6th International Workshop on Challenges of Large Applications in Distributed Environments, CLADE '08, pp.15-24, 2008.
DOI : 10.1145/1383529.1383533

M. Li, S. Sudharshan, A. R. Vazhkudai, F. Butt, X. Meng et al., Functional partitioning to optimize end-to-end performance on many-core architectures, International Conference for High Performance Computing, Networking, Storage and Analysis, pp.1-12, 2010.
DOI : 10.1109/sc.2010.28

URL : http://www.csm.ornl.gov/%7Evazhkuda/FP.pdf

C. Mommessin, M. Dreher, B. Raffin, and T. Peterka, Automatic data filtering for in situ workflows, IEEE Cluster, 2017.
DOI : 10.1109/cluster.2017.35

URL : https://hal.archives-ouvertes.fr/hal-01581032

X. Ma, J. Lee, and M. Winslett, High-Level Buffering for Hiding Periodic Output Cost in Scientific Simulations. Parallel and Distributed Systems, IEEE Transactions on, vol.17, issue.3, pp.193-204, 2006.

V. +-16]-preeti-malakar, C. Vishwanath, T. Knight, M. E. Munson, and . Papka, Optimal execution of co-analysis for large-scale molecular dynamics simulations, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '16, vol.60, pp.1-60, 2016.

P. Malakar, V. Vishwanath, T. Munson, C. Knight, M. Hereld et al., Optimal scheduling of in-situ analysis for large-scale scientific simulations, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '15, vol.52, pp.1-52, 2015.

K. Ma, C. Wang, H. Yu, and A. Tikhonova, Insitu processing and visualization for ultrascale simulations, Journal of Physics: Conference Series, vol.78, issue.1, p.12043, 2007.

P. Péba¨péba¨y, J. C. Bennett, D. Hollman, S. Treichler, P. S. Mccormick et al., Towards asynchronous manytask in situ data analysis using legion, 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp.1033-1037, 2016.

A. Singh, P. Balaji, and W. Feng, GePSeA: A GeneralPurpose Software Acceleration Framework for Lightweight Task Offloading, International Conference on Parallel Processing, pp.261-268, 2009.

. Sdd-+-18]-pradeep, P. Subedi, S. Davis, S. Duan, H. Klasky et al., Stacker: an autonomic data movement engine for extreme-scale data staging-based in situ workflows, Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'18), vol.65, pp.1-65, 2015.

D. Tao, S. Di, Z. Chen, and F. Cappello, Significantly improving lossy compression for scientific data sets based on multidimensional prediction and error-controlled quantization, 2017 IEEE International Parallel and Distributed Processing Symposium, pp.1129-1139, 2017.

C. A. Tu, D. W. Rendleman, R. O. Borhani, J. Dror, M. Ø. Gullingsrud et al., A Scalable Parallel Framework for Analyzing Terascale Molecular Dynamics Simulation Trajectories, Conference on Supercomputing, vol.56, p.12, 2008.

V. Vishwanath, M. Hereld, and M. E. Papka, Toward simulation-time data analysis and i/o acceleration on leadership-class systems, Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on, pp.9-14, 2011.

Y. Wang, G. Agrawal, T. Bicer, and W. Jiang, Smart: A mapreduce-like framework for in-situ scientific analytics, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '15, vol.51, pp.1-51, 2015.

B. Whitlock, J. M. Favre, and J. S. Meredith, Parallel in situ coupling of simulation with a fully featured visualization system, Proceedings of the 11th Eurographics Conference on Parallel Graphics and Visualization, EGPGV '11, pp.101-109, 2011.

S. Williams, A. Waterman, and D. Patterson, Roofline: an insightful visual performance model for multicore architectures, Communications of the ACM, vol.52, issue.4, pp.65-76, 2009.

H. Yu, C. Wang, R. W. Grout, J. H. Chen, and K. Ma, situ visualization for large-scale combustion simulations, vol.30, pp.45-57, 2010.

H. Fang-zheng, C. Abbasi, J. Docan, Q. Lofstead, S. Liu et al., PreDatAPreparatory Data Analytics on Peta-Scale Machines, Parallel Distributed Processing (IPDPS), pp.1-12, 2010.

. Zdp-+-12]-fan, C. Zhang, M. Docan, S. Parashar, N. Klasky et al., Enabling In-situ Execution of Coupled Scientific Workflow on Multi-core Platform, Parallel Distributed Processing Symposium (IPDPS), pp.1352-1363, 2012.

]. F. Zyh-+-13, H. Zheng, C. Yu, M. Hantas, G. Wolf et al., Goldrush: Resource efficient in situ scientific data analytics using fine-grained interference aware execution, SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pp.1-12, 2013.

F. Zheng, H. Zou, G. Eisenhauer, K. Schwan, M. Wolf et al., Flexio: I/o middleware for location-flexible scientific data analytics, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, pp.320-331, 2013.