THE PARSEC MACHINE: A NON-NEWTONIAN SUPRA-LINEAR SUPERCOMPUTER

Volume 2 (2), December 2019, Pages 122-140

Ulviya Abdulkarimova1,2, Anna Ouskova Leonteva1, Christian Rolando3, Anne Jeannin-Girardon1, Pierre Collet1


1Strasbourg University, France, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

2France UFAZ - Azerbaijan State University of Oil and Industry, Baku, Azerbaijan

3University of Lille, France


Abstract

This paper describes how transfer-learning can turn a Beowulf cluster into a full super-computer with supra-linear qualitative acceleration. Harmonic Analysis is used as a real-world example to show the kind of result that can be achieved with the proposed supercomputer architecture, that locally exploits absolute space-time parallelism on each machine (SIMD parallelism) and loosely-coupled relative space-time parallelization between different machines (loosely coupled MIMD).

Keywords:

Beowulf cluster, relative space-time, supra-linear acceleration, qualitative acceleration, GPGPU, loosely coupled machines, artificial evolution, transfer learning, harmonic analysis, super-resolution, non-uniform sampling, Fourier transform.

DOI: https://doi.org/10.32010/26166127.2019.2.2.122.140

 

Reference 

Lippert, T., Schilling, K., & Ueberholz, P. (1993). Science on the Connection Machine: Proceedings of the First European CM Users Meeting. (pp. 1-238).

Hull, M. E., Sweeney, P. J., & Crookes, D. (1994). Parallel Processing; The Transputer and Its Applications. Addison-Wesley Longman Publishing Co., Inc..

Krazit T. (2005, August, 17). First Dual-Core Pentium 4 a Rush Job, Intel Says. Retrieved from: https://www.pcworld.com/article/122236/article.html

Maitre, O., Baumes, L. A., Lachiche, N., Corma, A., & Collet, P. (2009, July). Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (pp. 1403-1410). ACM.

Newton, I. (1687). Philosophiae naturalis principia mathematica. Royal Society, London.

Clarke, S. Leibniz, G. W. (1717) A Collection of Papers, which passed between the late Learned Mr. Leibniz, and Dr. Clarke, In the Years 1715 and 1716. James Knapton, London.

Einstein, A. (1905). On the electrodynamics of moving bodies. Annalen Phys. 17, 891–921.

Einstein A., Podolsky B. and Rosen N. (1935). Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?, in Phys. Rev., vol. 47, p. 777-780

Yosinski, J., Clune, J., Bengio, Y., Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems. NIPS Foundation. 27, 3320–3328.

Fraser A.S. (1958). “Monte Carlo analyses of genetic models”. Nature. 181 (4603): 208-209. doi:10.1038/181208a0

Tsutsui, S., & Collet, P. (Eds.). (2013). Massively parallel evolutionary computation on GPGPUs (Vol. 453). Heidelberg: Springer.

Rechenberg, I. (1965). Cybernetic solution path of an experimental problem. Royal AircraftEstablishment, Farnborough p. Library Translation 1122.

Rechenberg, I. (1973). Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien Der Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart, (1973) (in German)

Schwefel, H.-P. (1965). Kybernetische evolution als strategie der experimentellen forschung in der stroemungstechnik. Dipl.-Ing. thesis (in German)

Schwefel, H. P. (1981). Numerical optimization of computer models. John Wiley & Sons, Inc.

Koza, J. (1992). GP: On the programming of computers by means of natural selection.

Collet P., Lutton E., Schoenauer M., Louchet J. (2000) Take It EASEA. In: Schoenauer M. et al. (eds) Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg

Maitre O. (2011). GPGPU for Evolutionary Algorithms, PhD thesis.

Maitre, O., Baumes, L. A., Lachiche, N., Corma, A., & Collet, P. (2009, July). Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (pp. 1403-1410). ACM.

Maitre, O., Lachiche, N., Clauss, P., Baumes, L., Corma, A., & Collet, P. (2009, August). Efficient parallel implementation of evolutionary algorithms on GPGPU cards. In European Conference on Parallel Processing (pp. 974-985). Springer, Berlin, Heidelberg.

Becker, D. J., Sterling, T., Savarese, D., Dorband, J. E., Ranawak, U. A., & Packer, C. V. (1995, August). BEOWULF: A parallel workstation for scientific computation. In Proceedings, International Conference on Parallel Processing (Vol. 95, pp. 11-14).

Chiron, L., van Agthoven, M. A., Kieffer, B., Rolando, C., & Delsuc, M. A. (2014). Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry. Proceedings of the National Academy of Sciences, 111(4), 1385-1390.

Flynn, M. J. (1972). Some computer organizations and their effectiveness. IEEE transactions on computers, 100(9), 948-960.

Choy, W. Y., & Sanctuary, B. C. (1998). Using genetic algorithms with a priori knowledge for quantitative NMR signal analysis. Journal of Chemical Information and Computer Sciences, 38(4), 685-690.

Zamanan, N., Sykulski, J. K., & Al-Othman, A. K. (2006, September). Real coded genetic algorithm compared to the classical method of fast fourier transform in harmonics analysis. In Proceedings of the 41st International Universities Power Engineering Conference (Vol. 3, pp. 1021-1025). IEEE.

Gustafson, J. L. (1988). Reevaluating Amdahl’s law. Communications of the ACM, 31(5), 532-533.

Amdahl, G. M. (1967, April). Validity of the single processor approach to achieving large scale computing capabilities. In Proceedings of the April 18-20, 1967, spring joint computer conference (pp. 483-485). ACM.

Smith, D. F., Podgorski, D. C., Rodgers, R. P., Blakney, G. T., & Hendrickson, C. L. (2018). 21 tesla FT-ICR mass spectrometer for ultrahigh-resolution analysis of complex organic mixtures. Analytical chemistry, 90(3), 2041-2047.