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Quantifying Computational Effort Required for Stochastic Averages.

Andrew J Schultz1, David A Kofke1

  • 1Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York , Buffalo, New York 14260-4200, United States.

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|November 20, 2015
PubMed
Summary
This summary is machine-generated.

We introduce a difficulty index to measure the computational effort for stochastic averages in molecular modeling. This metric helps optimize and compare methods, improving understanding of performance factors.

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Scientific computing

Background:

  • Stochastic averages are crucial in molecular modeling for calculating macroscopic properties from microscopic simulations.
  • Assessing the computational cost of these averages is essential for efficient research and development.

Purpose of the Study:

  • To propose a novel metric, the "difficulty index," for quantifying the computational effort required to compute stochastic averages.
  • To provide a standardized measure for comparing the performance of different molecular modeling techniques.

Main Methods:

  • Defining the difficulty index based on CPU time, observed uncertainty, and the characteristic scale of the computed quantity.
  • Applying the index to analyze the performance of various computational approaches.

Main Results:

  • The difficulty index effectively quantifies the computational resources needed for stochastic averages.
  • The metric highlights the influence of models, algorithms, implementations, and hardware on performance.

Conclusions:

  • The proposed difficulty index serves as a valuable tool for focusing optimization efforts in molecular modeling.
  • Widespread application of this metric can enhance the understanding and comparison of computational performance across different platforms and methods.