Radiation transport in a planar layer with an account of nonlinear absorption and scattering

1969 ◽  
Vol 10 (6) ◽  
pp. 634-639 ◽  
Author(s):  
�. P. Zege
Keyword(s):  
Nonlinear Absorption ◽  
Radiation Transport ◽  
Planar Layer

Comparison of Radiation Transport Codes for Solar Particle Events Space Environment: II

2009 ◽  
Author(s):  
Ram Tripathi ◽  
Lawrence Townsend ◽  
Tony Gabriel ◽  
Lawrence PIinsky ◽  
Tony Slaba
Keyword(s):  
Radiation Transport ◽  
Space Environment ◽  
Solar Particle Events ◽  
Solar Particle

A Monte Carlo study of radiation transport through multileaf collimators

2001 ◽  
Vol 28 (12) ◽  
pp. 2497-2506 ◽  
Author(s):  
Jong Oh Kim ◽  
Jeffrey V. Siebers ◽  
Paul J. Keall ◽  
Mark R. Arnfield ◽  
Radhe Mohan
Keyword(s):  
Monte Carlo ◽  
Radiation Transport ◽  
Monte Carlo Study ◽  
Multileaf Collimators

PENGEOM – A general-purpose geometry package for Monte Carlo simulation of radiation transport in complex material structures (New Version Announcement)

2021 ◽  
pp. 107962
Author(s):  
Julio Almansa ◽  
Francesc Salvat-Pujol ◽  
Gloria Díaz-Londoño ◽  
Artur Carnicer ◽  
Antonio M. Lallena ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Radiation Transport ◽  
General Purpose ◽  
Complex Material

Self-focusing of cosh-Gaussian laser beam in collisional plasma: effect of nonlinear absorption

2021 ◽  
Author(s):  
Naveen Gupta ◽  
Sandeep Kumar ◽  
A Gnaneshwaran ◽  
Sanjeev Kumar ◽  
Suman Choudhry
Keyword(s):  
Laser Beam ◽  
Nonlinear Absorption ◽  
Collisional Plasma ◽  
Gaussian Laser Beam ◽  
Self Focusing ◽  
Plasma Effect

scholarly journals Machine Learning–enabled Scalable Performance Prediction of Scientific Codes

2021 ◽  
Vol 31 (2) ◽  
pp. 1-28
Author(s):  
Gopinath Chennupati ◽  
Nandakishore Santhi ◽  
Phill Romero ◽  
Stephan Eidenbenz
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Prediction Models ◽  
Radiation Transport ◽  
Discrete Event ◽  
Basic Block ◽  
Distribution Models ◽  
Scientific Application ◽  
High Level ◽  
Access Patterns

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

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