On averaging the scattering properties of a heterogeneous medium in a model of penetrating radiation transport

The amyl graphic behaviour for products like carboxymetil starch obtained in different reaction conditions was looked into. The procedure specific feature is the chemical modification that takes place in a heterogeneous medium though the reaction of the reactant starch particle in indestructible conditions.

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Characterization of Flagellar Propulsion of Soft Microrobotic Sperm in a Viscous Heterogeneous Medium

Frontiers in Robotics and AI ◽

10.3389/frobt.2019.00065 ◽

2019 ◽

Vol 6 ◽

Cited By ~ 2

Author(s):

Islam S. M. Khalil ◽

Anke Klingner ◽

Youssef Hamed ◽

Veronika Magdanz ◽

Mohamed Toubar ◽

...

Keyword(s):

Heterogeneous Medium

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A Monte Carlo study of radiation transport through multileaf collimators

Medical Physics ◽

10.1118/1.1420734 ◽

2001 ◽

Vol 28 (12) ◽

pp. 2497-2506 ◽

Cited By ~ 79

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

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

Computer Physics Communications ◽

10.1016/j.cpc.2021.107962 ◽

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

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Machine Learning–enabled Scalable Performance Prediction of Scientific Codes

ACM Transactions on Modeling and Computer Simulation ◽

10.1145/3450264 ◽

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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