
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Kai Diethelm, "The Limits of Reproducibility in Numerical Simulation," Computing in Science and Engineering, vol. 14, no. 1, pp. 6472, Jan.Feb., 2012.  
BibTex  x  
@article{ 10.1109/MCSE.2011.21, author = {Kai Diethelm}, title = {The Limits of Reproducibility in Numerical Simulation}, journal ={Computing in Science and Engineering}, volume = {14}, number = {1}, issn = {15219615}, year = {2012}, pages = {6472}, doi = {http://doi.ieeecomputersociety.org/10.1109/MCSE.2011.21}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  MGZN JO  Computing in Science and Engineering TI  The Limits of Reproducibility in Numerical Simulation IS  1 SN  15219615 SP64 EP72 EPD  6472 A1  Kai Diethelm, PY  2012 KW  Parallel algorithms KW  numerical analysis KW  scientific computing VL  14 JA  Computing in Science and Engineering ER   
Modern computational simulation's increasing and mainly speedoriented use of HPC systems often conflicts with the goal of making research reproducible. Indeed, the simulations that result from HPC use often behave reproducibly in only a limited way. As a discussion of this phenomenon's technical background describes, the problems entailed will be very difficult to overcome.
1. D.E. Knuth, "Literate Programming," Computer J., vol. 27, no. 2, 1984, pp. 97–111.
2. J.B. Buckheit and D.L. Donoho, "WaveLab and Reproducible Research," Wavelets and Statistics, A. Antoniadis, and G. Oppenheim eds., Springer Verlag, 1995, pp. 55–81.
3. M. Schwab, M. K arrenbach, and J. Claerbout, "Making Scientific Computations Reproducible," Computing in Science & Eng., vol. 2, no. 6, 2000, pp. 61–67.
4. J.J. Quirk, "Computational Science: 'Same Old Silence, Same Old Mistakes'; Something More is Needed," Adaptive Mesh Refinement—Theory and Applications, T. Plewa, T. Linde, and V.G. Weirs eds., Springer Verlag, 2005, pp. 3–28.
5. P., Vandewalle, J. Kovačević, and M. Vetterli, "What, Why and How of Reproducible Research in Signal Processing," IEEE Signal Processing, vol. 26, no. 3, 2009, pp. 37–47.
6. S. Fomel and J.C. Claerbout eds., "Reproducible Research," special issue, Computing in Science & Eng., vol. 11, no. 1, 2009, pp. 5–40.
7. C.W. Ahn, Advances in Evolutionary Algorithms, Springer Verlag, 2006.
8. M. Evans, and T. Swartz, Approximating Integrals via Monte Carlo and Deterministic Methods, Oxford Univ. Press, 2000.
9. H. Tschaetsch, Metal Forming Practise, Springer Verlag, 2006.
10. O. Schenk and K. Gärtner, "Solving Unsymmetric Sparse Systems of Linear Equations with Pardiso," J. Future Generation Computer Systems, vol. 20, 2004, pp. 475–487.
11. G. Meurant, The Lanczos and Conjugate Gradient Algorithms, SIAM, 2006.
12. O. Schenk, M. Bollhöfer, and R.A. Römer, "On LargeScale Diagonalization Techniques for the Anderson Model of Localization," SIAM Rev., vol. 50, no. 1, 2008, pp. 91–112.
13. Y.K. Zhu and W.B. Hayes, "Algorithm 908: Online Exact Summation of FloatingPoint Streams," ACM Trans. Mathematical Software, vol. 37, no. 3, 2010; doi:10.1145/1824801.1824815.
14. N.J. Higham, "The Accuracy of Floating Point Summation," SIAM J. Scientific Computing, vol. 14, 1993, pp. 783–799.
15. R.E. Moore, R.B. Kearfott, and M.J. Cloud, Introduction to Interval Analysis, SIAM, 2009.
16. L. Biegler et al., eds., LargeScale Inverse Problems and Quantification of Uncertainty, John Wiley & Sons, 2011.
17. O.P. Le Maître and O.M. Knio, Spectral Methods for Uncertainty Quantification, Springer Verlag, 2010.
18. Y. He and C.H.Q. Ding, "Using Accurate Arithmetics to Improve Numerical Reproducibility and Stability in Parallel Applications," J. Supercomputing, vol. 18, no. 3, 2001, pp. 259–277.