0000000001320195

AUTHOR

Richard Kreckel

showing 3 related works from this author

Parallelization of adaptive MC integrators

1997

Monte Carlo (MC) methods for numerical integration seem to be embarassingly parallel on first sight. When adaptive schemes are applied in order to enhance convergence however, the seemingly most natural way of replicating the whole job on each processor can potentially ruin the adaptive behaviour. Using the popular VEGAS-Algorithm as an example an economic method of semi-micro parallelization with variable grain-size is presented and contrasted with another straightforward approach of macro-parallelization. A portable implementation of this semi-micro parallelization is used in the xloops-project and is made publicly available.

Variable (computer science)Hardware and ArchitectureComputer scienceAdaptive behaviourIntegratorMonte Carlo methodConvergence (routing)FOS: Physical sciencesGeneral Physics and AstronomyParallel computingComputational Physics (physics.comp-ph)Physics - Computational PhysicsNumerical integrationComputer Physics Communications
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Introduction to the GiNaC Framework for Symbolic Computation within the C++ Programming Language

2002

AbstractThe traditional split into a low level language and a high level language in the design of computer algebra systems may become obsolete with the advent of more versatile computer languages. We describe GiNaC, a special-purpose system that deliberately denies the need for such a distinction. It is entirely written in C++and the user can interact with it directly in that language. It was designed to provide efficient handling of multivariate polynomials, algebras and special functions that are needed for loop calculations in theoretical quantum field theory. It also bears some potential to become a more general purpose symbolic package.

Computer Science - Symbolic ComputationI.1.3FOS: Computer and information sciencesFor loopTheoretical computer scienceAlgebra and Number TheoryFOS: Physical sciencesI.1.1; I.1.3Symbolic Computation (cs.SC)Computational Physics (physics.comp-ph)Symbolic computationI.1.1High Energy Physics - PhenomenologyComputational MathematicsHigh Energy Physics - Phenomenology (hep-ph)General purposeHigh-level programming languageSpecial functionsFourth-generation programming languagePhysics - Computational PhysicsC programming languageLow-level programming languageMathematicsJournal of Symbolic Computation
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Parallelization of adaptive MC integrators

2019

Abstract Monte Carlo (MC) methods for numerical integration seem to be embarrassingly parallel on first sight. When adaptive schemes are applied in order to enhance convergence however, the seemingly most natural way of replicating the whole job on each processor can potentially ruin the adaptive behaviour. Using the popular VEGAS-Algorithm as an example an economic method of semi-micro parallelization with variable grain-size is presented and contrasted with another straightforward approach of macro-... Title of program: pvegas.c Catalogue Id: ADGU_v1_0 Nature of problem Monte Carlo (MC) methods for numerical integration seem to be embarassingly parallel on first sight. When adaptive schem…

Computational MethodComputational PhysicsFOS: Electrical engineering electronic engineering information engineeringProgramming LanguageComputer HardwareSoftware
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