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

  • Udaya A. Ranawake
  • Patrick Lenders
  • Stephen M. Goodnick
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 113)


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.


Problem Size Parallel Performance Monte Carlo Algorithm Single Processor Parallel Speedup 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • Udaya A. Ranawake
    • 1
  • Patrick Lenders
    • 1
  • Stephen M. Goodnick
    • 1
  1. 1.Department of Electrical and Computer EngineeringOregon State UniversityCorvallisUSA

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