scholarly journals On Vectorization of Monte-Carlo Algorithm Solving Classical Boltzmann Equation

2020 ◽  
pp. 13-21
Author(s):  
Dmitry Zav'yalov ◽  
Vitaliy Egunov ◽  
Vladimir Konchenkov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Boltzmann Equation ◽  
Equations Of Motion ◽  
Monte Carlo Algorithm ◽  
Simulation Algorithm ◽  
External Fields ◽  
Kinetic Coefficients ◽  
Classical Boltzmann Equation

The vectorization of calculations in the Monte Carlo simulation algorithm of kinetic coefficients of solids under the influence of homogeneous external fields on the sample is discussed. It is shown that the vectorization of calculations related to the solution of the equations of motion of particles allows to obtain an acceleration from 10 to 30 %.

A high-order Monte Carlo algorithm for the direct simulation of Boltzmann equation

2012 ◽  
Vol 231 (14) ◽  
pp. 4578-4596 ◽  
Author(s):  
Pouyan Jahangiri ◽  
Amir Nejat ◽  
Jila Samadi ◽  
Ali Aboutalebi
Keyword(s):  
Monte Carlo ◽  
Boltzmann Equation ◽  
High Order ◽  
Monte Carlo Algorithm ◽  
Direct Simulation

Assessing Curriculum Efficiency Through Monte Carlo Simulation

2018 ◽  
Vol 22 (4) ◽  
pp. 597-610
Author(s):  
David Torres ◽  
Jorge Crichigno ◽  
Carmella Sanchez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Distribution ◽  
Relative Frequency ◽  
Monte Carlo Algorithm ◽  
Pass Rate ◽  
Random Numbers ◽  
Pass Rates ◽  
Low Pass ◽  
Relative Frequency Distribution

A Monte Carlo algorithm is designed to predict the average time to graduate by enrolling virtual students in a degree plan. The algorithm can be used to improve graduation rates by identifying bottlenecks in a degree plan (e.g., low pass rate courses and prerequisites). Random numbers are used to determine whether students pass or fail classes by comparing them to institutional pass rates. Courses cannot be taken unless prerequisites and corequisites are satisfied. The output of the algorithm generates a relative frequency distribution which plots the number of students who graduate by semester. Pass rates of courses can be changed to determine the courses that have the greatest impact on the time to graduate. Prerequisites can also be removed to determine whether certain prerequisites significantly affect the time to graduate.

PARALLELIZATION OF THE ISING SIMULATION

1993 ◽  
Vol 04 (06) ◽  
pp. 1131-1135 ◽  
Author(s):  
NOBUYASU ITO
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Algorithm ◽  
Efficient Simulation ◽  
Relaxation Study ◽  
Single Processor ◽  
Non Equilibrium

The parallelization aspect of the Ising Monte Carlo simulation is discussed. It is shown that most of the theoretically interesting simulations now are suitable for the trivial parallelization, that is, the Ising simulation is ideally parallelizable. Furthermore, presently most efficient simulation algorithm for single processor is also a kind of trivial paralellization. Results on the non-equilibrium critical relaxation study is included as an example.

scholarly journals Exact Monte Carlo simulation of killed diffusions

2008 ◽  
Vol 40 (1) ◽  
pp. 273-291 ◽  
Author(s):  
Bruno Casella ◽  
Gareth O. Roberts
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Algorithm ◽  
Barrier Option ◽  
Double Barrier ◽  
One Dimensional ◽  
Finite Dimensional ◽  
Theoretical Predictions ◽  
Efficient Alternative ◽  
Barrier Option Pricing

We describe and implement a novel methodology for Monte Carlo simulation of one-dimensional killed diffusions. The proposed estimators represent an unbiased and efficient alternative to current Monte Carlo estimators based on discretization methods for the cases when the finite-dimensional distributions of the process are unknown. For barrier option pricing in finance, we design a suitable Monte Carlo algorithm both for the single barrier case and the double barrier case. Results from numerical investigations are in excellent agreement with the theoretical predictions.

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