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

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  • William L. Goffe
  • Michael Creel

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

Suggested Citation

  • William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:438
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    References listed on IDEAS

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    1. Donghoon Lee & Matthew Wiswall, 2007. "A Parallel Implementation of the Simplex Function Minimization Routine," Computational Economics, Springer;Society for Computational Economics, vol. 30(2), pages 171-187, September.
    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. Ekvall, Niklas, 1994. "Experiences in the Pricing of Trivariate Contingent Claims with Finite Difference Methods on a Massively Parallel Computer," Computational Economics, Springer;Society for Computational Economics, vol. 7(2), pages 63-72.
    4. Kontoghiorghes, Erricos J, 2000. "Parallel Strategies for Solving SURE Models with Variance Inequalities and Positivity of Correlations Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 15(1-2), pages 89-106, April.
    5. Giorgio Pauletto & Manfred Gilli, 2000. "Parallel Krylov Methods for Econometric Model Simulation," Computational Economics, Springer;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. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    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. Swann, Christopher A, 2002. "Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI," Computational Economics, Springer;Society for Computational Economics, vol. 19(2), pages 145-178, April.
    10. 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).
    11. Beaumont, Paul M & Bradshaw, Patrick T, 1995. "A Distributed Parallel Genetic Algorithm for Solving Optimal Growth Models," Computational Economics, Springer;Society for Computational Economics, vol. 8(3), pages 159-179, August.
    12. Michael Creel, 2005. "ParallelKnoppix," Grecs Computer Code 003.05, Research Group in Computation and Simulations (GRECS).
    13. 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).
    14. Christopher Ferrall, 2005. "Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution," Computational Economics, Springer;Society for Computational Economics, vol. 25(4), pages 343-379, June.
    15. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 26(2), pages 107-128, October.
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    Cited by:

    1. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    2. 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.
    3. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 151-182, August.
    4. Michael C. Hatcher & Eric M. Scheffel, 2016. "Solving the Incomplete Markets Model in Parallel Using GPU Computing and the Krusell–Smith Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 569-591, December.
    5. Matt Dziubinski & Stefano Grassi, 2014. "Heterogeneous Computing in Economics: A Simplified Approach," Computational Economics, Springer;Society for Computational Economics, vol. 43(4), pages 485-495, April.
    6. Lilia Maliar, 2015. "Assessing gains from parallel computation on a supercomputer," Economics Bulletin, AccessEcon, vol. 35(1), pages 159-167.
    7. 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.
    8. Dimitris Kremmydas & M.I. Haque & Stelios Rozakis, 2011. "Enhancing Web-Spatial DSS interactivity with parallel computing: The case of bio-energy economic assessment in Greece," Working Papers 2011-2, Agricultural University of Athens, Department Of Agricultural Economics.
    9. Yongyang Cai & Kenneth Judd & Greg Thain & Stephen Wright, 2015. "Solving Dynamic Programming Problems on a Computational Grid," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 261-284, February.
    10. Michael Creel, 2016. "A Note on Julia and MPI, with Code Examples," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 535-546, October.

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    More about this item

    Keywords

    parallel computing; clusters; grid computing;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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