L. Dagum and R. Menon, OpenMP: an industry standard API for shared-memory programming, IEEE Comput. Sci. Eng, vol.5, pp.46-55, 1998.

A. Sampson, A. Baixo, B. Ransford, T. Moreau, J. Yip et al., Accept: A programmer-guided compiler framework for practical approximate computing, p.1, 2015.

M. Carbin, S. Misailovic, and M. C. Rinard, Verifying quantitative reliability for programs that execute on unreliable hardware, ACM SIGPLAN Notices, vol.48, pp.33-52, 2013.

J. Ansel, L. Wong, Y. Chan, C. Olszewski, M. Edelman et al., Language and compiler support for auto-tuning variable-accuracy algorithms, Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization, pp.85-96, 2011.

C. Burstedde, L. C. Wilcox, and O. Ghattas, p4est: Scalable algorithms for parallel adaptive mesh refinement on forests of octrees, SIAM J. Sci. Comput, vol.33, pp.1103-1133, 2011.

B. S. Kirk, J. W. Peterson, R. H. Stogner, and G. F. Carey, libMesh: A C++ Library for Parallel Adaptive Mesh Refinement/Coarsening Simulations, Eng. Comput, vol.22, pp.237-254, 2006.

D. R. Musser, G. J. Derge, and A. Saini, STL Tutorial and Reference Guide: C++ Programming with the Standard Template Library, 2009.

K. Kambatla, G. Kollias, V. Kumar, and A. Grama, Trends in big data analytics, J. Parallel Distrib. Comput, vol.74, pp.2561-2573, 2014.

S. Mittal, A survey of techniques for approximate computing, ACM Comput. Surv. (CSUR), vol.48, p.62, 2016.

L. Tornvist, P. Vartia, and Y. Vartia, How should relative changes be measured?, Am Statist, vol.39, pp.43-46, 1985.

A. Hore and D. Ziou, Image quality metrics: Psnr vs. ssim, 20th International Conference on Pattern Recognition (ICPR), pp.2366-2369, 2010.
DOI : 10.1109/icpr.2010.579

R. P. Feynman, R. B. Leighton, and M. Sands, The new millennium edition: mainly mechanics, radiation, and heat, vol.I, 2011.

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), 2006.

P. Feautrier, Some efficient solutions to the affine scheduling problem. I. One-dimensional time, Int. J. Parallel Program, vol.21, pp.313-347, 1992.
DOI : 10.1007/bf01407835

P. Feautrier, Some efficient solutions to the affine scheduling problem. II. multidimensional time, Int. J. Parallel Program, vol.21, pp.389-420, 1992.
DOI : 10.1007/bf01407835

C. Bastoul, Mapping deviation: A technique to adapt or to guard loop transformation intuitions for legality, Proceedings of the 25th International Conference on Compiler Construction, pp.229-239, 2016.
DOI : 10.1145/2892208.2892216

URL : https://hal.archives-ouvertes.fr/hal-01271998

T. Grosser, A. Groesslinger, and C. Lengauer, Pollyperforming polyhedral optimizations on a low-level intermediate representation, Parallel Process. Lett, vol.22, p.1250010, 2012.

C. Bastoul, Extracting polyhedral representation from high level languages, 2008.

S. Verdoolaege and T. Grosser, Polyhedral extraction tool, Second International Workshop on Polyhedral Compilation Techniques (IMPACT'12), pp.1-16, 2012.

C. Bastoul, Code generation in the polyhedral model is easier than you think, Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques, pp.7-16, 2004.
DOI : 10.1109/pact.2004.1342537

URL : https://hal.archives-ouvertes.fr/hal-00017260

F. Quilleri, S. Rajopadhye, and D. Wilde, Generation of efficient nested loops from polyhedra, Int. J. Parallel Program, vol.28, pp.469-498, 2000.

U. Banerjee, Loop transformations for restructuring compilers: the foundations, 2007.
DOI : 10.1007/b102311

G. Strang, , vol.4, 2009.

M. Wolfe, More iteration space tiling, Proceedings of the 1989 ACM/IEEE conference on Supercomputing, pp.655-664, 1989.
DOI : 10.1145/76263.76337

M. E. Wolf and M. S. Lam, A data locality optimizing algorithm, ACM Sigplan Notices, vol.26, pp.30-44, 1991.
DOI : 10.1145/113446.113449

S. Verdoolaege, isl: An integer set library for the polyhedral model, ICMS, vol.6327, pp.299-302, 2010.
DOI : 10.1007/978-3-642-15582-6_49

W. Kelly, V. Maslov, W. Pugh, E. Rosser, T. Shpeisman et al., The omega calculator and library, vol.20742, p.18, 1996.

M. J. Berger and P. Colella, Local adaptive mesh refinement for shock hydrodynamics, J. Comput. Phys, vol.82, pp.64-84, 1989.
DOI : 10.1016/0021-9991(89)90035-1

URL : https://zenodo.org/record/1253914/files/article.pdf

A. Sampson, W. Dietl, E. Fortuna, E. Fortuna, D. Gnanapragasam et al., Enerj: Approximate data types for safe and general low-power computation, Proceedings of the 32Nd ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI '11, pp.164-174, 2011.

T. Yeh, P. Faloutsos, M. Ercegovac, S. Patel, and G. Reinman, The art of deception: Adaptive precision reduction for area efficient physics acceleration, 40th Annual IEEE/ACM International Symposium on Microarchitecture, pp.394-406, 2007.

S. Sidiroglou-douskos, S. Misailovic, H. Hoffmann, and M. Rinard, Managing performance vs. accuracy trade-offs with loop perforation, Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering, pp.124-134, 2011.

S. Misailovic, D. M. Roy, and M. C. Rinard, Probabilistically accurate program transformations, Static Analysis, pp.316-333, 2011.
DOI : 10.1007/978-3-642-23702-7_24

URL : http://people.csail.mit.edu/rinard/paper/sas11.pdf

M. Rinard, Probabilistic accuracy bounds for faulttolerant computations that discard tasks, Proceedings of the 20th Annual International Conference on Supercomputing, ICS '06, ACM, pp.324-334, 2006.
DOI : 10.1145/1183401.1183447

URL : http://www.cag.lcs.mit.edu/~rinard/paper/ics06.ps

A. Rahimi, L. Benini, and R. K. Gupta, Spatial memoization: Concurrent instruction reuse to correct timing errors in simd architectures, IEEE Trans. Circuits Syst. II: Express Briefs, vol.60, pp.847-851, 2013.
DOI : 10.1109/tcsii.2013.2281934

URL : http://mesl.ucsd.edu/site/pubs/TCAS13_Abbas.pdf

D. Michie, 'memo'' functions and machine learning, Nature, vol.218, p.19, 1968.

J. Ansel, C. Chan, Y. L. Wong, M. Olszewski, Q. Zhao et al., PetaBricks: a language and compiler for algorithmic choice, vol.44, 2009.

M. Schmitt, P. Helluy, C. Bastoul, and C. Bastoul, Adaptive code refinement: A compiler technique and extensions to generate self-tuning applications, HiPC 2017-24th International Conference on High Performance Computing, Data, and Analytics, pp.1-10, 2017.
DOI : 10.1109/hipc.2017.00028

URL : https://hal.archives-ouvertes.fr/hal-01655459

C. Bastoul, A. Cohen, S. Girbal, S. Sharma, and O. Temam, Putting polyhedral loop transformations to work, Languages and Compilers for Parallel Computing, pp.209-225, 2004.
DOI : 10.1007/978-3-540-24644-2_14

URL : https://hal.archives-ouvertes.fr/inria-00071681

S. Verdoolaege and T. Grosser, Polyhedral extraction tool, Second International Workshop on Polyhedral Compilation Techniques (IMPACT'12), 2012.

A. Schrijver, Theory of linear and integer programming, 1998.

U. Bondhugula, A. Hartono, J. Ramanujam, and P. Sadayappan, A practical automatic polyhedral parallelizer and locality optimizer, SIGPLAN Notices, vol.43, pp.101-113, 2008.
DOI : 10.1145/1379022.1375595

URL : http://www.cse.ohio-state.edu/~bondhugu/publications/uday-pldi08.pdf

L. Pouchet, C. Bastoul, A. Cohen, and J. Cavazos, Iterative optimization in the polyhedral model: Part II. Multidimensional time, ACM SIGPLAN Notices, vol.43, pp.90-100, 2008.
DOI : 10.1145/1375581.1375594

URL : https://hal.archives-ouvertes.fr/hal-01257273

W. Bielecki and M. Palkowski, Tiling of arbitrarily nested loops by means of the transitive closure of dependence graphs, Int. J. Appl. Math. Comput. Sci. (AMCS), vol.26, pp.919-939, 2016.

J. Stam, Stable fluids, Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH '99, pp.121-128, 1999.
DOI : 10.1145/311535.311548

J. Stam, Real-time fluid dynamics for games, Proceedings of the Game Developer Conference, p.25, 2003.

A. F. Oskooi, D. Roundy, M. Ibanescu, P. Bermel, J. D. Joannopoulos et al., Meep: A flexible free-software package for electromagnetic simulations by the FDTD method, Comput. Phys. Commun, vol.181, pp.687-702, 2010.
DOI : 10.1016/j.cpc.2009.11.008

URL : http://dspace.mit.edu/bitstream/1721.1/60946/1/Johnson_MEEP%20A.pdf

W. Gosper and R. , Exploiting regularities in large cellular spaces, Phys. D: Nonlinear Phenom, vol.10, pp.75-80, 1984.

J. Meng, S. Chakradhar, and A. Raghunathan, Best-effort parallel execution framework for recognition and mining applications, IPDPS'09, pp.1-12, 2009.

M. Schmitt, ACR compiler and runtime, 2017.

S. Campanoni, G. Holloway, G. Wei, and D. M. Brooks, HELIX-UP: Relaxing program semantics to unleash parallelization, IEEE/ACM CGO, pp.235-245, 2015.

S. Byna, J. Meng, A. Raghunathan, S. Chakradhar, and S. Cadambi, Best-effort semantic document search on gpus, Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, pp.86-93, 2010.

M. Samadi, J. Lee, A. Jamshidi, A. Hormati, D. Mahlke et al., Sage: Self-tuning approximation for graphics engines, IEEE/ACM Intl. Symp. on Microarchitecture, pp.13-24, 2013.

V. K. Chippa, D. Mohapatra, A. Raghunathan, K. Roy, and S. T. Chakradhar, Scalable effort hardware design: Exploiting algorithmic resilience for energy efficiency, Proceedings of the 47th Design Automation Conference, pp.555-560, 2010.

Y. Fang, H. W. Li, and X. W. Li, Softpcm: Enhancing energy efficiency and lifetime of phase change memory in video applications via approximate write, Test Symposium (ATS), pp.131-136, 2012.

A. Sampson, J. Nelson, K. Strauss, and L. Ceze, Approximate storage in solid-state memories, ACM Trans. Comput. Syst, vol.32, pp.1-9, 2014.

S. Misailovic, M. Carbin, S. Achour, Z. C. Qi, and M. C. Rinard, Chisel: Reliability-and accuracy-aware optimization of approximate computational kernels, ACM SIG-PLAN Notices, vol.49, pp.309-328, 2014.

H. Hoffmann, S. Sidiroglou, M. Carbin, M. Carbin, S. Misailovic et al., Dynamic knobs for responsive power-aware computing, Proceedings of the Sixteenth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS XVI, ACM, pp.199-212, 2011.

M. Samadi, D. A. Jamshidi, J. Lee, and S. Mahlke, Paraprox: Pattern-based approximation for data parallel applications, Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '14, ACM, p.3550, 2014.

V. K. Chippa, S. T. Chakradhar, K. Roy, and A. Raghunathan, Analysis and characterization of inherent application resilience for approximate computing, Proceedings of the 50th Annual Design Automation Conference, p.113, 2013.

W. Baek and T. M. Chilimbi, Green: A framework for supporting energy-conscious programming using controlled approximation, Proceedings of the 31st ACM SIG-PLAN Conference on Programming Language Design and Implementation, PLDI '10, pp.198-209, 2010.

J. Bornholt, T. Mytkowicz, and K. S. Mckinley, Uncertain: A first-order type for uncertain data, Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '14, ACM, pp.51-66, 2014.

J. Sorber, A. Kostadinov, M. Garber, M. Brennan, M. D. Corner et al., Eon: A language and runtime system for perpetual systems, Proceedings of the 5th International Conference on Embedded Networked Sensor Systems, SenSys '07, pp.161-174, 2007.