Skip to main content

Parallelization of Monte Carlo Algorithms in Semiconductor Device Physics on Hypercube Multiprocessors

  • Chapter
Computational Electronics

Abstract

We have developed efficient parallel solutions of Monte Carlo algorithms for analyzing numerical models for charge transport used in semiconductor device physics. The algorithms were implemented on a 64 node hypercube multiprocessor and time measurements were made as both the problem size and number of processors are varied. A 64 node processor ensemble is measured to be 35 to 52 times as fast as a single processor when the problem size for the ensemble is fixed, and 61 to 63 times as fast as a single processor when problem size per processor is fixed. The latter measure, denoted scaled speedup, is shown to be better suited for denoting the parallel performance of Monte Carlo algorithms than the traditional measure of parallel speedup. Finally, an analysis of the test results are presented.

Supported by NSF grant number ECS-8821107.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. Jacoboi and L. Reggiani, “ The Monte Carlo method for the solution of charge transport in semiconductors with applications to covalent materials”, Reviews of Modern Physics, Vol. 55, No. 3, July 1983, pp 645–705

    Article  Google Scholar 

  2. H.D. Rees,“Calculation of distributed functions by exploiting the stability of steady state”, J. Phys. Chem. Solids, Vol. 30, 1969, pp 643–655

    Article  Google Scholar 

  3. W.R. Martin, T.C. Wan, T.S. Abdel-Rahmen and T.N. Mudge, “Monte Carlo photon transport on shared memory and distributed memory parallel processors”, Journal of Supercomputer Applications, Vol 1, No. 3, pp 57–74, 1988

    Article  Google Scholar 

  4. P. Frederickson, R. Hiromoto, T.L. Jordan, B. Smith and T. Warnock, “Pseudo-random trees in Monte Carlo”, Parallel Computing, 1(1984), pp 175–180

    Article  MATH  Google Scholar 

  5. J.L. Gustafson, G.R. Montry and R.E. Benner, “Development of parallel methods for a 1024-processor hypercube”, SIAM Journal on Scientific and Statistical Computing, Vol. 9, No. 4, July 1988

    Google Scholar 

  6. G. Amdahl, “Validity of the single-processor approach to achieving large-scale computer capabilities”, AFIPS Conf. Proc, 30(1967), pp 483–485

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer Science+Business Media New York

About this chapter

Cite this chapter

Ranawake, U.A., Lenders, P., Goodnick, S.M. (1991). Parallelization of Monte Carlo Algorithms in Semiconductor Device Physics on Hypercube Multiprocessors. In: Hess, K., Leburton, J.P., Ravaioli, U. (eds) Computational Electronics. The Springer International Series in Engineering and Computer Science, vol 113. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2124-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-2124-9_26

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5122-9

  • Online ISBN: 978-1-4757-2124-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics