8,156 research outputs found

    Hierarchical Parallelisation of Functional Renormalisation Group Calculations -- hp-fRG

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    The functional renormalisation group (fRG) has evolved into a versatile tool in condensed matter theory for studying important aspects of correlated electron systems. Practical applications of the method often involve a high numerical effort, motivating the question in how far High Performance Computing (HPC) can leverage the approach. In this work we report on a multi-level parallelisation of the underlying computational machinery and show that this can speed up the code by several orders of magnitude. This in turn can extend the applicability of the method to otherwise inaccessible cases. We exploit three levels of parallelisation: Distributed computing by means of Message Passing (MPI), shared-memory computing using OpenMP, and vectorisation by means of SIMD units (single-instruction-multiple-data). Results are provided for two distinct High Performance Computing (HPC) platforms, namely the IBM-based BlueGene/Q system JUQUEEN and an Intel Sandy-Bridge-based development cluster. We discuss how certain issues and obstacles were overcome in the course of adapting the code. Most importantly, we conclude that this vast improvement can actually be accomplished by introducing only moderate changes to the code, such that this strategy may serve as a guideline for other researcher to likewise improve the efficiency of their codes

    Living Standards, Scarce Resources and Immigration: An Interview With Labor Economist Vernon M. Briggs, Jr.

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    [Excerpt] Immigration reformers are drawn to the issue by myriad paths. Some arrive with a burning concern over the prospect of a billion person nation in a lifetime. The wildlife and natural heritage of the nation will be irretrievably altered by this expansive footprint. Others are motivated by present concerns over dwindling water reserves, energy, pauperized soils, solid waste, urban sprawl, congestion, and maybe just because our national parks are being loved to death. Vernon Briggs, Ph.D., a liberal Democrat, comes to immigration reform through an interest in labor economics. He responds to an interest in the underprivileged American citizen. His compassion runs deep. As revealed in this interview, exposure to John F. Kennedy during college days placed a claim upon his conscience. He has not escaped from this claim during the past 4.5 decades. His support of the underprivileged citizenry has found prolific expression in countless academic journals. His interest in conferring dignity upon labor is more than academic. It is a passion. And his passion endures. In this issue, The Social Contract honors the integrity, compassion, resourcefulness, and genius of Professor Briggs of Cornell University

    Spectral function at high missing energies and momenta

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    The nuclear spectral function at high missing energies and momenta has been determined from a self-consistent calculation of the Green's function in nuclear matter using realistic nucleon-nucleon interactions. The results are compared with recent experimental data derived from (e,epe,e'p) reactions on 12C^{12}C. A rather good agreement is obtained if the Green's functions are calculated in a non-perturbative way.Comment: 10 pages, 3 figure

    Quasi-particle functional Renormalisation Group calculations in the two-dimensional half-filled Hubbard model at finite temperatures

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    We present a highly parallelisable scheme for treating functional Renormalisation Group equations which incorporates a quasi-particle-based feedback on the flow and provides direct access to real-frequency self-energy data. This allows to map out the boundaries of Fermi-liquid regimes and to study the effect of quasi-particle degradation near Fermi liquid instabilities. As a first application, selected results for the two-dimensional half-filled perfectly nested Hubbard model are shown

    Generalized least squares can overcome the critical threshold in respondent-driven sampling

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    In order to sample marginalized and/or hard-to-reach populations, respondent-driven sampling (RDS) and similar techniques reach their participants via peer referral. Under a Markov model for RDS, previous research has shown that if the typical participant refers too many contacts, then the variance of common estimators does not decay like O(n1)O(n^{-1}), where nn is the sample size. This implies that confidence intervals will be far wider than under a typical sampling design. Here we show that generalized least squares (GLS) can effectively reduce the variance of RDS estimates. In particular, a theoretical analysis indicates that the variance of the GLS estimator is O(n1)O(n^{-1}). We then derive two classes of feasible GLS estimators. The first class is based upon a Degree Corrected Stochastic Blockmodel for the underlying social network. The second class is based upon a rank-two model. It might be of independent interest that in both model classes, the theoretical results show that it is possible to estimate the spectral properties of the population network from the sampled observations. Simulations on empirical social networks show that the feasible GLS (fGLS) estimators can have drastically smaller error and rarely increase the error. A diagnostic plot helps to identify where fGLS will aid estimation. The fGLS estimators continue to outperform standard estimators even when they are built from a misspecified model and when there is preferential recruitment.Comment: Submitte
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