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Home All issues Volume 73 (2018) Oil & Gas Science and Technology - Rev. IFP Energies nouvelles, 73 (2018) 52 References
Numerical methods and HPC
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Issue
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles
Volume 73, 2018
Numerical methods and HPC
Article Number 52
Number of page(s) 22
DOI https://doi.org/10.2516/ogst/2018049
Published online 14 November 2018
  • Dagum L., Menon R. (1998) OpenMP: an industry standard API for shared-memory programming, IEEE Comput. Sci. Eng. 5, 1, 46–55. [CrossRef] [Google Scholar]
  • Sampson A., Baixo A., Ransford B., Moreau T., Yip J., Ceze L., Oskin M. (2015) Accept: A programmer-guided compiler framework for practical approximate computing, University of Washington Technical Report UW-CSE-15-01, p. 1. [Google Scholar]
  • Carbin M., Misailovic S., Rinard M.C. (2013) Verifying quantitative reliability for programs that execute on unreliable hardware, ACM SIGPLAN Notices 48, 33–52. [CrossRef] [Google Scholar]
  • Ansel J., Lok Wong Y., Chan C., Olszewski M., Edelman A., Amarasinghe S. (2011) Language and compiler support for auto-tuning variable-accuracy algorithms, in: Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization, pp. 85–96. [Google Scholar]
  • Burstedde C., Wilcox L.C., Ghattas O. (2011) p4est: Scalable algorithms for parallel adaptive mesh refinement on forests of octrees, SIAM J. Sci. Comput. 33, 1103–1133. [CrossRef] [MathSciNet] [Google Scholar]
  • Kirk B.S., Peterson J.W., Stogner R.H., Carey G.F. (2006) libMesh: A C++ Library for Parallel Adaptive Mesh Refinement/Coarsening Simulations, Eng. Comput. 22, 3–4, 237–254. [CrossRef] [Google Scholar]
  • Musser D.R., Derge G.J., Saini A. (2009) STL Tutorial and Reference Guide: C++ Programming with the Standard Template Library, 3rd edn., Addison-Wesley Professional, London, UK. [Google Scholar]
  • Kambatla K., Kollias G., Kumar V., Grama A. (2014) Trends in big data analytics, J. Parallel Distrib. Comput. 74, 7, 2561–2573. [CrossRef] [Google Scholar]
  • Mittal S. (2016) A survey of techniques for approximate computing, ACM Comput. Surv. (CSUR) 48, 4, 62. [Google Scholar]
  • Tornvist L., Vartia P., Vartia Y. (1985) How should relative changes be measured? Am Statist. 39, 43–46. [Google Scholar]
  • Hore A., Ziou D. (2010) Image quality metrics: Psnr vs. ssim, 20th International Conference on Pattern Recognition (ICPR), pp. 2366–2369. [Google Scholar]
  • Feynman R.P., Leighton R.B., Sands M. (2011) The Feynman lectures on physics, Vol. I: The new millennium edition: mainly mechanics, radiation, and heat, Basic Books. [Google Scholar]
  • Bishop C.M. (2006) Pattern Recognition and Machine Learning (Information Science and Statistics), Springer Verlag, Berlin, Heidelberg. [Google Scholar]
  • Feautrier P. (1992) Some efficient solutions to the affine scheduling problem. I. One-dimensional time, Int. J. Parallel Program. 21, 5, 313–347. [CrossRef] [Google Scholar]
  • Feautrier P. (1992) Some efficient solutions to the affine scheduling problem. II. multidimensional time, Int. J. Parallel Program. 21, 6, 389–420. [CrossRef] [Google Scholar]
  • Bastoul C. (2016) Mapping deviation: A technique to adapt or to guard loop transformation intuitions for legality, in: Proceedings of the 25th International Conference on Compiler Construction, Barcelona, Spain, pp. 229–239. [Google Scholar]
  • Grosser T., Groesslinger A., Lengauer C. (2012) Polly – performing polyhedral optimizations on a low-level intermediate representation, Parallel Process. Lett. 22, 1250010. [CrossRef] [Google Scholar]
  • Bastoul C. (2008) Extracting polyhedral representation from high level languages, Tech. Rep. Related to the Clan tool, LRI, Paris-Sud University. [Google Scholar]
  • Verdoolaege S., Grosser T. (2012) Polyhedral extraction tool, in: Second International Workshop on Polyhedral Compilation Techniques (IMPACT’12), Paris, France, pp. 1–16. [Google Scholar]
  • Bastoul C. (2004) Code generation in the polyhedral model is easier than you think, in: Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques, pp. 7–16. [Google Scholar]
  • Quilleri F., Rajopadhye S., Wilde D. (2000) Generation of efficient nested loops from polyhedra, Int. J. Parallel Program. 28, 5, 469–498. [CrossRef] [Google Scholar]
  • Banerjee U. (2007) Loop transformations for restructuring compilers: the foundations, Springer Science & Business Media, Massachusetts, USA. [Google Scholar]
  • Strang G. (2009) Introduction to linear algebra, Vol. 4, Wellesley-Cambridge Press, Wellesley, MA. [Google Scholar]
  • Wolfe M. (1989) More iteration space tiling, in: Proceedings of the 1989 ACM/IEEE conference on Supercomputing, pp. 655–664. [Google Scholar]
  • Wolf M.E., Lam M.S. (1991) A data locality optimizing algorithm, ACM Sigplan Notices 26, 30–44. [CrossRef] [Google Scholar]
  • Verdoolaege S. (2010) isl: An integer set library for the polyhedral model, ICMS 6327, 299–302. [Google Scholar]
  • Kelly W., Maslov V., Pugh W., Rosser E., Shpeisman T., Wonnacott D. (1996) The omega calculator and library, version 1.1.0, College Park, MD, 20742:18 [Google Scholar]
  • Berger M.J., Colella P. (1989) Local adaptive mesh refinement for shock hydrodynamics, J. Comput. Phys. 82, 64–84. [NASA ADS] [CrossRef] [Google Scholar]
  • Sampson A., Dietl W., Fortuna E., Fortuna E., Gnanapragasam D., Ceze L., Grossman D. (2011) Enerj: Approximate data types for safe and general low-power computation, in: Proceedings of the 32Nd ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ‘11, San Jose, California, USA, pp. 164–174. [Google Scholar]
  • Yeh T., Faloutsos P., Ercegovac M., Patel S., Reinman G. (2007) The art of deception: Adaptive precision reduction for area efficient physics acceleration, 40th Annual IEEE/ACM International Symposium on Microarchitecture, 394–406. [Google Scholar]
  • Sidiroglou-Douskos S., Misailovic S., Hoffmann H., Rinard M. (2011) Managing performance vs. accuracy trade-offs with loop perforation, in: Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering, pp. 124–134. [Google Scholar]
  • Misailovic S., Roy D.M., Rinard M.C. (2011) Probabilistically accurate program transformations, in: Yahav E. (eds), Static Analysis, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 316–333. [CrossRef] [Google Scholar]
  • Rinard M. (2006) Probabilistic accuracy bounds for fault-tolerant computations that discard tasks, in: Proceedings of the 20th Annual International Conference on Supercomputing, ICS ’06, ACM, Cairns, Queensland, Australia, pp. 324–334. [CrossRef] [Google Scholar]
  • Rahimi A., Benini L., Gupta R.K. (2013) Spatial memoization: Concurrent instruction reuse to correct timing errors in simd architectures, IEEE Trans. Circuits Syst. II: Express Briefs 60, 12, 847–851. [CrossRef] [Google Scholar]
  • Michie D. (1968) “memo” functions and machine learning, Nature 218, 5136, 19. [CrossRef] [Google Scholar]
  • Ansel J., Chan C., Wong Y.L., Olszewski M., Zhao Q., Edelman A., Amarasinghe S. (2009) PetaBricks: a language and compiler for algorithmic choice, Vol. 44, ACM. [Google Scholar]
  • Schmitt M., Helluy P., Bastoul C., Bastoul C. (2017) 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, Jaipur, India, pp. 1–10. [Google Scholar]
  • Bastoul C., Cohen A., Girbal S., Sharma S., Temam O. (2004) Putting polyhedral loop transformations to work, in: Rauchwerger L. (ed.), Languages and Compilers for Parallel Computing, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 209–225. [CrossRef] [Google Scholar]
  • Verdoolaege S., Grosser T. (2012) Polyhedral extraction tool, Second International Workshop on Polyhedral Compilation Techniques (IMPACT’12), Paris, France. [Google Scholar]
  • Schrijver A. (1998) Theory of linear and integer programming, John Wiley & Sons. [Google Scholar]
  • Bondhugula U., Hartono A., Ramanujam J., Sadayappan P. (2008) A practical automatic polyhedral parallelizer and locality optimizer, SIGPLAN Notices 43, 6, 101–113. [CrossRef] [Google Scholar]
  • Pouchet L.-N., Bastoul C., Cohen A., Cavazos J. (2008) Iterative optimization in the polyhedral model: Part II. Multidimensional time, ACM SIGPLAN Notices 43, 90–100. [CrossRef] [Google Scholar]
  • Bielecki W., Palkowski M. (2016) Tiling of arbitrarily nested loops by means of the transitive closure of dependence graphs, Int. J. Appl. Math. Comput. Sci. (AMCS) 26, 4, 919–939. [CrossRef] [Google Scholar]
  • Stam J. (1999) Stable fluids, in: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ‘99, ACM Press/Addison-Wesley Publishing Co, New York, NY, USA, pp. 121–128. [CrossRef] [Google Scholar]
  • Stam J. (2003) Real-time fluid dynamics for games, in: Proceedings of the Game Developer Conference, 25. [Google Scholar]
  • Oskooi A.F., Roundy D., Ibanescu M., Bermel P., Joannopoulos J.D., Johnson S.G. (2010) Meep: A flexible free-software package for electromagnetic simulations by the FDTD method, Comput. Phys. Commun. 181, 3, 687–702. [CrossRef] [Google Scholar]
  • Wm Gosper R. (1984) Exploiting regularities in large cellular spaces, Phys. D: Nonlinear Phenom. 10, 1–2, 75–80. [CrossRef] [Google Scholar]
  • Meng J., Chakradhar S., Raghunathan A. (2009) Best-effort parallel execution framework for recognition and mining applications, IPDPS’09, pp. 1–12. [Google Scholar]
  • Schmitt M. (2017) ACR compiler and runtime, http://gauvain.u-strasbg.fr/~schmitt/acr [Google Scholar]
  • Campanoni S., Holloway G., Wei G.-Y., Brooks D.M. (2015) HELIX-UP: Relaxing program semantics to unleash parallelization, in: IEEE/ACM CGO, San Francisco, USA, pp. 235–245. [Google Scholar]
  • Byna S., Meng J., Raghunathan A., Chakradhar S., Cadambi S. (2010) Best-effort semantic document search on gpus, in: Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, pp. 86–93. [Google Scholar]
  • Samadi M., Lee J., Jamshidi A., Anoushe Hormati D., Mahlke S. (2013) Sage: Self-tuning approximation for graphics engines, in: MICRO’13 IEEE/ACM Intl. Symp. on Microarchitecture, Davis, California, pp. 13–24. [Google Scholar]
  • Chippa V.K., Mohapatra D., Raghunathan A., Roy K., Chakradhar S.T. (2010) Scalable effort hardware design: Exploiting algorithmic resilience for energy efficiency, in: Proceedings of the 47th Design Automation Conference, pp. 555–560. [Google Scholar]
  • Fang Y., Li H.W., Li X.W. (2012) Softpcm: Enhancing energy efficiency and lifetime of phase change memory in video applications via approximate write, in: Test Symposium (ATS), 2012 IEEE 21st Asian, pp. 131–136. [Google Scholar]
  • Sampson A., Nelson J., Strauss K., Ceze L. (2014) Approximate storage in solid-state memories, ACM Trans. Comput. Syst. 32, 3, 1–9. [CrossRef] [Google Scholar]
  • Misailovic S., Carbin M., Achour S., Qi Z.C., Rinard M.C. (2014) Chisel: Reliability-and accuracy-aware optimization of approximate computational kernels, ACM SIG-PLAN Notices 49, 309–328. [CrossRef] [Google Scholar]
  • Hoffmann H., Sidiroglou S., Carbin M., Carbin M., Misailovic S., Agarwal A., Rinard M.C. (2011) Dynamic knobs for responsive power-aware computing, in: Proceedings of the Sixteenth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS XVI, ACM, Newport Beach, California, USA, pp. 199–212. [CrossRef] [Google Scholar]
  • Samadi M., Jamshidi D.A., Lee J., Mahlke S. (2014) Paraprox: Pattern-based approximation for data parallel applications, in: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’14, ACM, Salt Lake City, Utah, USA, pp. 3550. [Google Scholar]
  • Chippa V.K., Chakradhar S.T., Roy K., Raghunathan A. (2013) Analysis and characterization of inherent application resilience for approximate computing, in: Proceedings of the 50th Annual Design Automation Conference, p. 113. [Google Scholar]
  • Baek W., Chilimbi T.M. (2010) Green: A framework for supporting energy-conscious programming using controlled approximation, in: Proceedings of the 31st ACM SIG-PLAN Conference on Programming Language Design and Implementation, PLDI ’10, ACM, Toronto, Ontario, Canada, pp. 198–209. [CrossRef] [Google Scholar]
  • Bornholt J., Mytkowicz T., McKinley K.S. (2014) Uncertain: A first-order type for uncertain data, in: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’14, ACM, Salt Lake City, Utah, USA, pp. 51–66. [CrossRef] [Google Scholar]
  • Sorber J., Kostadinov A., Garber M., Brennan M., Corner M.D., Berger E.D. (2007) Eon: A language and runtime system for perpetual systems, in: Proceedings of the 5th International Conference on Embedded Networked Sensor Systems, SenSys ‘07, ACM, Sydney, Australia, pp. 161–174. [CrossRef] [Google Scholar]

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