Search

For highly complex problems, such as the post-Newtonian formulation of compact binaries, the two-particle method may be a better, or even the only, choice to compute the Lyapunov characteristic exponent (LCE). This method avoids the complex calculations of variational equations compared with the variational method. However, the two-particle method sometimes provides spurious estimates to LCEs. In this paper, we first analyze the equivalence in the definition of LCE between the variational and two-particle methods for Hamiltonian systems. Then, we develop a criterion to determine the reliability of LCEs computed by the two-particle method by considering the magnitude of the initial tangent (or separation) vector ξ 0 (or δ 0 ), renormalization time interval τ , machine precision ε , and global truncation error ϵ T . The reliable Lyapunov characteristic indicators estimated by the two-particle method form a V-shaped region, which is restricted by d 0 , ε , and ϵ T . Finally, the numerical experiments with the Hénon–Heiles system, the spinning compact binaries, and the post-Newtonian circular restricted three-body problem strongly support the theoretical results. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the architecture, the development and the implementation of Janus II, a new generation application-driven number cruncher optimized for Monte Carlo simulations of spin systems (mainly spin glasses). This domain of computational physics is a recognized grand challenge of high-performance computing: the resources necessary to study in detail theoretical models that can make contact with experimental data are by far beyond those available using commodity computer systems. On the other hand, several specific features of the associated algorithms suggest that unconventional computer architectures–that can be implemented with available electronics technologies–may lead to order of magnitude increases in performance, reducing to acceptable values on human scales the time needed to carry out simulation campaigns that would take centuries on commercially available machines. Janus II is one such machine, recently developed and commissioned, that builds upon and improves on the successful JANUS machine, which has been used for physics since 2008 and is still in operation today. This paper describes in detail the motivations behind the project, the computational requirements, the architecture and the implementation of this new machine and compares its expected performances with those of currently available commercial systems. [Copyright &y& Elsevier]

Abstract: In this paper we describe a HepML format and a corresponding C++ library developed for keeping complete description of parton level events in a unified and flexible form. HepML tags contain enough information to understand what kind of physics the simulated events describe and how the events have been prepared. A HepML block can be included into event files in the LHEF format. The structure of the HepML block is described by means of several XML Schemas. The Schemas define necessary information for the HepML block and how this information should be located within the block. The library libhepml is a C++ library intended for parsing and serialization of HepML tags, and representing the HepML block in computer memory. The library is an API for external software. For example, Matrix Element Monte Carlo event generators can use the library for preparing and writing a header of an LHEF file in the form of HepML tags. In turn, Showering and Hadronization event generators can parse the HepML header and get the information in the form of C++ classes. libhepml can be used in C++, C, and Fortran programs. All necessary parts of HepML have been prepared and we present the project to the HEP community. Program summary: Program title: libhepml Catalogue identifier: AEGL_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEGL_v1_0.html Program obtainable from: CPC Program Library, Queen''s University, Belfast, N. Ireland Licensing provisions: GNU GPLv3 No. of lines in distributed program, including test data, etc.: 138 866 No. of bytes in distributed program, including test data, etc.: 613 122 Distribution format: tar.gz Programming language: C++, C Computer: PCs and workstations Operating system: Scientific Linux CERN 4/5, Ubuntu 9.10 RAM: 1 073 741 824 bytes (1 Gb) Classification: 6.2, 11.1, 11.2 External routines: Xerces XML library (http://xerces.apache.org/xerces-c/), Expat XML Parser (http://expat.sourceforge.net/) Nature of problem: Monte Carlo simulation in high energy physics is divided into several stages. Various programs exist for these stages. In this article we are interested in interfacing different Monte Carlo event generators via data files, in particular, Matrix Element (ME) generators and Showering and Hadronization (SH) generators. There is a widely accepted format for data files for such interfaces – Les Houches Event Format (LHEF). Although information kept in an LHEF file is enough for proper working of SH generators, it is insufficient for understanding how events in the LHEF file have been prepared and which physical model has been applied. In this paper we propose an extension of the format for keeping additional information available in generators. We propose to add a new information block, marked up with XML tags, to the LHEF file. This block describes events in the file in more detail. In particular, it stores information about a physical model, kinematical cuts, generator, etc. This helps to make LHEF files self-documented. Certainly, HepML can be applied in more general context, not in LHEF files only. Solution method: In order to overcome drawbacks of the original LHEF accord we propose to add a new information block of HepML tags. HepML is an XML-based markup language. We designed several XML Schemas for all tags in the language. Any HepML document should follow rules of the Schemas. The language is equipped with a library for operation with HepML tags and documents. This C++ library, called libhepml, consists of classes for HepML objects, which represent a HepML document in computer memory, parsing classes, serializating classes, and some auxiliary classes. Restrictions: The software is adapted for solving problems, described in the article. There are no additional restrictions. Running time: Tests have been done on a computer with Intel(R) Core(TM)2 Solo, 1.4 GHz. Parsing of a HepML file: 6 ms (size of the HepML files is 12.5 Kb) Writing of a HepML block to file: 14 ms (file size 12.5 Kb) Merging of two HepML blocks and writing to file: 18 ms (file size – 25.0 Kb). [Copyright &y& Elsevier]

Abstract: In this paper we present FeynRules, a new Mathematica package that facilitates the implementation of new particle physics models. After the user implements the basic model information (e.g., particle content, parameters and Lagrangian), FeynRules derives the Feynman rules and stores them in a generic form suitable for translation to any Feynman diagram calculation program. The model can then be translated to the format specific to a particular Feynman diagram calculator via FeynRules translation interfaces. Such interfaces have been written for CalcHEP/CompHEP, FeynArts/FormCalc, MadGraph/MadEvent and Sherpa, making it possible to write a new model once and have it work in all of these programs. In this paper, we describe how to implement a new model, generate the Feynman rules, use a generic translation interface, and write a new translation interface. We also discuss the details of the FeynRules code. Program summary: Program title: FeynRules Catalogue identifier: AEDI_v1_0 Program summary URL:: http://cpc.cs.qub.ac.uk/summaries/AEDI_v1_0.html Program obtainable from: CPC Program Library, Queen''s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 15 980 No. of bytes in distributed program, including test data, etc.: 137 383 Distribution format: tar.gz Programming language: Mathematica Computer: Platforms on which Mathematica is available Operating system: Operating systems on which Mathematica is available Classification: 11.1, 11.2, 11.6 Nature of problem: Automatic derivation of Feynman rules from a Lagrangian. Implementation of new models into Monte Carlo event generators and FeynArts. Solution method: FeynRules works in two steps: Restrictions: The Lagrangian must fulfill basic QFT requirements, such as Lorentz and gauge invariance. Only fields with spin 0, 1/2, 1 and 2 are implemented. Unusual features: Translation interfaces to FeynArts, CalcHEP/CompHEP, MadGraph and Sherpa exist. Running time: The running time depends on the complexity of the Lagrangian, and varies from seconds (Standard Model) to minutes (more complicated models, like the 3-Site Model). [Copyright &y& Elsevier]

In this paper, an atomistic geometrical model for the B-DNA configuration is explained. This model accounts for five organization levels of the DNA, up to the 30 nm chromatin fiber. However, fragments of this fiber can be used to construct the whole genome. The algorithm developed in this work is capable to determine which is the closest atom with respect to an arbitrary point in space. It can be used in any application in which a DNA geometrical model is needed, for instance, in investigations related to the effects of ionizing radiations on the human genetic material. Successful consistency checks were carried out to test the proposed model. Program summary: Program title: FindClosestAtom Catalogue identifier: AEPZ_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEPZ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 1245 No. of bytes in distributed program, including test data, etc.: 6574 Distribution format: tar.gz Programming language: FORTRAN. Computer: Any. Operating system: Multi-platform. RAM: 2 Gb Classification: 3. Nature of problem: The Monte Carlo method is used to simulate the interaction of ionizing radiation with the human genetic material in order to determine DNA damage yields per unit absorbed dose. To accomplish this task, an algorithm to determine if a given energy deposition lies within a given target is needed. This target can be an atom or any other structure of the genetic material. Solution method: This is a stand-alone subroutine describing an atomic-resolution geometrical model of the B-DNA configuration. It is able to determine the closest atom to an arbitrary point in space. This model accounts for five organization levels of the human genetic material, from the nucleotide pair up to the 30 nm chromatin fiber. This subroutine carries out a series of coordinate transformations to find which is the closest atom containing an arbitrary point in space. Atom sizes are according to the corresponding van der Waals radii. Restrictions: The geometrical model presented here does not include the chromosome organization level but it could be easily build up by using fragments of the 30 nm chromatin fiber. Unusual features: To our knowledge, this is the first open source atomic-resolution DNA geometrical model developed for DNA–radiation interaction Monte Carlo simulations. In our tests, the current model took into account the explicit position of about 56×106 atoms, although the user may enhance this amount according to the necessities. Running time: This subroutine can process about 2 million points within a few minutes in a typical current computer. [ABSTRACT FROM AUTHOR]

Abstract: The effective delayed neutron fraction plays an important role in kinetics and static analysis of the reactor physics experiments. It is used as reactivity unit referred to as “dollar”. Usually, it is obtained by computer simulation due to the difficulty in measuring it experimentally. In 1965, Keepin proposed a method, widely used in the literature, for the calculation of the effective delayed neutron fraction . This method requires calculation of the adjoint neutron flux as a weighting function of the phase space inner products and is easy to implement by deterministic codes. With Monte Carlo codes, the solution of the adjoint neutron transport equation is much more difficult because of the continuous-energy treatment of nuclear data. Consequently, alternative methods, which do not require the explicit calculation of the adjoint neutron flux, have been proposed. In 1997, Bretscher introduced the -ratio method for calculating the effective delayed neutron fraction; this method is based on calculating the multiplication factor of a nuclear reactor core with and without the contribution of delayed neutrons. The multiplication factor set by the delayed neutrons (the delayed multiplication factor) is obtained as the difference between the total and the prompt multiplication factors. Using Monte Carlo calculation Bretscher evaluated the as the ratio between the delayed and total multiplication factors (therefore the method is often referred to as the -ratio method). In the present work, the -ratio method is applied by Monte Carlo (MCNPX) and deterministic (PARTISN) codes. In the latter case, the ENDF/B nuclear data library of the fuel isotopes (235U and 238U) has been processed by the NJOY code with and without the delayed neutron data to prepare multi-group WIMSD neutron libraries for the lattice physics code DRAGON, which was used to generate the PARTISN macroscopic cross sections. In recent years Meulekamp and van der Marck in 2006 and Nauchi and Kameyama in 2005 proposed new methods for the effective delayed neutron fraction calculation with only one Monte Carlo computer simulation, compared with the -ratio method which require two criticality calculations. In this paper, the Meulekamp/Marck and Nauchi/Kameyama methods are applied for the first time by the MCNPX computer code and the results obtained by all different methods are compared. [Copyright &y& Elsevier]

Abstract: Interest in the study of structural and energetic properties of highly quantum clusters, such as inert gas clusters has motivated the development of a hardware-accelerated framework for Quantum Monte Carlo simulations. In the Quantum Monte Carlo method, the properties of a system of atoms, such as the ground-state energies, are averaged over a number of iterations. Our framework is aimed at accelerating the computations in each iteration of the QMC application by offloading the calculation of properties, namely energy and trial wave function, onto reconfigurable hardware. This gives a user the capability to run simulations for a large number of iterations, thereby reducing the statistical uncertainty in the properties, and for larger clusters. This framework is designed to run on the Cray XD1 high performance reconfigurable computing platform, which exploits the coarse-grained parallelism of the processor along with the fine-grained parallelism of the reconfigurable computing devices available in the form of field-programmable gate arrays. In this paper, we illustrate the functioning of the framework, which can be used to calculate the energies for a model cluster of helium atoms. In addition, we present the capabilities of the framework that allow the user to vary the chemical identities of the simulated atoms. Program summary: Program title: Hardware Accelerated Quantum Monte Carlo (HAQMC) Catalogue identifier: AEEP_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEEP_v1_0.html Program obtainable from: CPC Program Library, Queen''s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 691 537 No. of bytes in distributed program, including test data, etc.: 5 031 226 Distribution format: tar.gz Programming language: C/C++ for the QMC application, VHDL and Xilinx 8.1 ISE/EDK tools for FPGA design and development Computer: Cray XD1 consisting of a dual-core, dualprocessor AMD Opteron 2.2 GHz with a Xilinx Virtex-4 (V4LX160) or Xilinx Virtex-II Pro (XC2VP50) FPGA per node. We use the compute node with the Xilinx Virtex-4 FPGA Operating system: Red Hat Enterprise Linux OS Has the code been vectorised or parallelized?: Yes Classification: 6.1 Nature of problem: Quantum Monte Carlo is a practical method to solve the Schrödinger equation for large many-body systems and obtain the ground-state properties of such systems. This method involves the sampling of a number of configurations of atoms and averaging the properties of the configurations over a number of iterations. We are interested in applying the QMC method to obtain the energy and other properties of highly quantum clusters, such as inert gas clusters. Solution method: The proposed framework provides a combined hardware–software approach, in which the QMC simulation is performed on the host processor, with the computationally intensive functions such as energy and trial wave function computations mapped onto the field-programmable gate array (FPGA) logic device attached as a co-processor to the host processor. We perform the QMC simulation for a number of iterations as in the case of our original software QMC approach, to reduce the statistical uncertainty of the results. However, our proposed HAQMC framework accelerates each iteration of the simulation, by significantly reducing the time taken to calculate the ground-state properties of the configurations of atoms, thereby accelerating the overall QMC simulation. We provide a generic interpolation framework that can be extended to study a variety of pure and doped atomic clusters, irrespective of the chemical identities of the atoms. For the FPGA implementation of the properties, we use a two-region approach for accurately computing the properties over the entire domain, employ deep pipelines and fixed-point for all our calculations guaranteeing the accuracy required for our simulation. [Copyright &y& Elsevier]

Abstract: We report extensive computer simulations of the Vicsek model [V.M. Vicsek, et al., Phys. Rev. Lett. 75 (1995) 1226], aimed to describe the onset of ordering within the low-velocity regime of the collective displacement of self-driven agents. The VM assumes that each agent adopts the average direction of movement of its neighbors, perturbed by an external noise. The existence of a phase transition between a state of ordered collective displacement (low-noise limit) and a disordered regime (high-noise limit) is most likely the most distinctive feature of the VM. In this paper, after briefly discussing the critical nature of the transition we focus our attention on the behavior of the VM in the low-velocity () regime for the displacement of the agents. In fact, while the XY model, which could somewhat be considered as the equilibrium counterpart of the VM, does not exhibit order in dimensions, an intriguing feature of the VM is precisely the onset of order. Since in the XY model the particles remain fixed in the lattice, we show that the understanding of the limit is relevant in order to explain the different behavior of both models. [Copyright &y& Elsevier]