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Multi-core CPUs, Clusters, and Grid Computing: A Tutorial

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  • Michael Creel

    ()

  • William Goffe

    ()

Abstract

The nature of computing is changing and it poses both challenges and opportunities for economists. Instead of increasing clock speed, future microprocessors will have "multi-cores" with separate execution units. "Threads" or other multi-processing techniques that are rarely used today are required to take full advantage of them. Beyond one machine, it has become easy to harness multiple computers to work in clusters. Besides dedicated clusters, they can be made up of unused lab computers or even your colleagues' machines. We will give live demos of multi-core and clusters and will describe grid computing (multiple clusters that could span the Internet). OpenMP (open multi-processing) and MPI (message passing interface) are among the topics described and shown live

(This abstract was borrowed from another version of this item.)

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File URL: http://hdl.handle.net/10.1007/s10614-008-9143-5
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 32 (2008)
Issue (Month): 4 (November)
Pages: 353-382

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Handle: RePEc:kap:compec:v:32:y:2008:i:4:p:353-382

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: Multi-core; Cluster; OpenMP; MPI; Grid;

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References

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  1. Erricos Kontoghiorghes & Elias Dinenis & Dennis Parkinson, . "Parallel Strategies for Solving SURE Models with Variance Inequalities and Positivity of Correlations Constraints," Computing in Economics and Finance 1997 45, Society for Computational Economics.
  2. Michael Creel, 2007. "I ran four million probits last night: HPC clustering with ParallelKnoppix," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 215-223.
  3. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, March Cit.
  4. Jurgen A. Doornik & David F. Hendry & Neil Shephard, . "Computationally-intensive Econometrics using a Distributed Matrix-programming Language," Economics Papers 2001-W22, Economics Group, Nuffield College, University of Oxford.
  5. Giorgio Pauletto & Manfred Gilli, 2000. "Parallel Krylov Methods for Econometric Model Simulation," Computational Economics, Society for Computational Economics, vol. 16(1/2), pages 173-186, October.
  6. Nagurney, Anna, 1996. "Parallel computation," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 7, pages 335-404 Elsevier.
  7. Manfred Gilli & Giorgio Pauletto, 1993. "Econometric Model Simulation on Parallel Computers," Research Papers by the Department of Economics, University of Geneva 93.07, Département des Sciences Économiques, Université de Genève.
  8. Nagurney, Anna & Takayama, Takashi & Zhang, Ding, 1995. "Massively parallel computation of spatial price equilibrium problems as dynamical systems," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 3-37.
  9. Beaumont, Paul M & Bradshaw, Patrick T, 1995. "A Distributed Parallel Genetic Algorithm for Solving Optimal Growth Models," Computational Economics, Society for Computational Economics, vol. 8(3), pages 159-79, August.
  10. Donghoon Lee & Matthew Wiswall, 2007. "A Parallel Implementation of the Simplex Function Minimization Routine," Computational Economics, Society for Computational Economics, vol. 30(2), pages 171-187, September.
  11. Michael Creel, 2005. "ParallelKnoppix," Grecs Computer Code 003.05, Research Group in Computation and Simulations (GRECS).
  12. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," UFAE and IAE Working Papers 637.05, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  13. Swann, Christopher A, 2002. "Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI," Computational Economics, Society for Computational Economics, vol. 19(2), pages 145-78, April.
  14. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
  15. Michael Creel, 2004. "ParallelKnoppix - Rapid Deployment of a Linux Cluster for MPI Parallel Processing Using Non-Dedicated Computers," UFAE and IAE Working Papers 625.04, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  16. Ekvall, Niklas, 1994. "Experiences in the Pricing of Trivariate Contingent Claims with Finite Difference Methods on a Massively Parallel Computer," Computational Economics, Society for Computational Economics, vol. 7(2), pages 63-72.
  17. Christopher Ferrall, 2005. "Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution," Computational Economics, Society for Computational Economics, vol. 25(4), pages 343-379, June.
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Citations

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Cited by:
  1. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
  2. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox," CREATES Research Papers 2013-09, School of Economics and Management, University of Aarhus.
  3. Bogdan OANCEA & Tudorel ANDREI & Raluca DRAGOESCU, 2012. "Cuda Based Computational Methods For Macroeconomic Forecasts," New Trends in Modelling and Economic Forecast (MEF 2011), ROMANIAN ACADEMY – INSTITUTE FOR ECONOMIC FORECASTING & "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 1(1), pages 42-53, January.
  4. D. Kremmydas & A. Petsakos & S. Rozakis, 2012. "Parametric Optimization of Linear and Non-Linear Models via Parallel Computing to Enhance Web-Spatial DSS Interactivity," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 4(1), pages 14-29, January.
  5. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox," Tinbergen Institute Discussion Papers 13-055/III, Tinbergen Institute.
  6. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Society for Computational Economics, vol. 40(2), pages 151-182, August.
  7. Lilia Maliar, 2013. "Assessing gains from parallel computation on supercomputers," Working Papers. Serie AD 2013-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).

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