The Accelerated Universe

Pope, Adrian; Habib, Salman; Lukić, Zarija; Daniel, David; Fasel, Patricia; Heitmann, Katrin; Desai, Nehal

doi:10.1109/mcse.2010.28

Cited by 23 publications

(22 citation statements)

References 4 publications

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“…The largest simulation (both with respect to volume and particle number) is carried out using our new Hardware/Hybrid Accelerated Cosmology Code (HACC) framework described in Habib et al (2009) and Pope et al (2010). This simulation covers a volume of (2 h −1 Gpc) 3 and evolves 2048 3 particles and was run on the hybrid supercomputer Cerrillos at Los Alamos National Laboratory.…”

Section: Simulation Suitementioning

confidence: 99%

Dark Matter Halo Profiles of Massive Clusters: Theory Versus Observations

Bhattacharya

Habib

Heitmann

et al. 2013

ApJ

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332

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Dark matter-dominated cluster-scale halos act as an important cosmological probe and provide a key testing ground for structure formation theory. Focusing on their mass profiles, we have carried out (gravity-only) simulations of the concordance ΛCDM cosmology, covering a mass range of 2 × 10 12 − 2 × 10 15 h −1 M ⊙ and a redshift range of z = 0 − 2, while satisfying the associated requirements of resolution and statistical control. When fitting to the Navarro-Frenk-White profile, our concentration-mass (c − M) relation differs in normalization and shape in comparison to previous studies that have limited statistics in the upper end of the mass range. We show that the flattening of the c − M relation with redshift is naturally expressed if c is viewed as a function of the peak height parameter, ν. Unlike the c − M relation, the slope of the c − ν relation is effectively constant over the redshift range z = 0 − 2, while the amplitude varies by ∼ 30% for massive clusters. This relation is, however, not universal: Using a simulation suite covering the allowed wCDM parameter space, we show that the c − ν relation varies by about ± 20% as cosmological parameters are varied. At fixed mass, the c(M) distribution is well-fit by a Gaussian with σ c / c ≃ 0.33, independent of the radius at which the concentration is defined, the halo dynamical state, and the underlying cosmology. We compare the ΛCDM predictions with observations of halo concentrations from strong lensing, weak lensing, galaxy kinematics, and X-ray data, finding good agreement for massive clusters (M vir > 4 × 10 14 h −1 M ⊙ ), but with some disagreements at lower masses. Because of uncertainty in observational systematics and modeling of baryonic physics, the significance of these discrepancies remains unclear.

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Section: Simulation Suitementioning

confidence: 99%

Dark Matter Halo Profiles of Massive Clusters: Theory Versus Observations

Bhattacharya

Habib

Heitmann

et al. 2013

ApJ

Self Cite

240

332

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“…As an early validation of its design philosophy, HACC was among the pioneering applications proven on the heterogeneous architecture of Roadrunner (Habib et al 2009;Pope et al 2010), the first computer to break the petaflop barrier. With its multi-algorithmic structure, HACC allows the coupling of MPI with a variety of local programming models -MPI+'X' -to readily adapt to different platforms.…”

Section: Hacc Design Motivations and Implementationmentioning

confidence: 99%

HACC: Simulating sky surveys on state-of-the-art supercomputing architectures

et al. 2016

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Current and future surveys of large-scale cosmic structure are associated with a massive and complex datastream to study, characterize, and ultimately understand the physics behind the two major components of the 'Dark Universe', dark energy and dark matter. In addition, the surveys also probe primordial perturbations and carry out fundamental measurements, such as determining the sum of neutrino masses. Large-scale simulations of structure formation in the Universe play a critical role in the interpretation of the data and extraction of the physics of interest. Just as survey instruments continue to grow in size and complexity, so do the supercomputers that enable these simulations. Here we report on HACC (Hardware/Hybrid Accelerated Cosmology Code), a recently developed and evolving cosmology N-body code framework, designed to run efficiently on diverse computing architectures and to scale to millions of cores and beyond. HACC can run on all current supercomputer architectures and supports a variety of programming models and algorithms. It has been demonstrated at scale on Cell-and GPU-accelerated systems, standard multicore node clusters, and Blue Gene systems. HACC's design allows for ease of portability, and at the same time, high levels of sustained performance on the fastest supercomputers available. We present a description of the design philosophy of HACC, the underlying algorithms and code structure, and outline implementation details for several specific architectures. We show selected accuracy and performance results from some of the largest high resolution cosmological simulations so far performed, including benchmarks evolving more than 3.6 trillion particles.

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“…Optical distortion caused by lensing is observed at all scales in the universe, making gravitational lensing a powerful tool for investigating dark matter and dark energy [27]. Gravitational lensing is simulated by computing the gravitational potential from the continuous density field of dark matter particles modeled by an N-body cosmological simulation [38]. To compute an accurate lensing simulation, one must have a density field that is low in noise, that reflects the underlying continuous density function, and that scales to the size of the simulation.…”

Section: Tessellation Methodsmentioning

confidence: 99%

“…One may define analytical synthetic density functions to produce desired data characteristics. For example, molecular dynamics [22,44] and cosmology simulations [2,38], while both particle methods, have widely different behavior. We begin with a simple symmetrical density profile whose parameters are chosen to mimic the density profile of a given problem.…”

Section: Tessellation Methodsmentioning

confidence: 99%

Self-Adaptive Density Estimation of Particle Data

Peterka¹,

Croubois²,

Li³

et al. 2016

SIAM J. Sci. Comput.

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Abstract. We present a study of density estimation, the conversion of discrete particle positions to a continuous field of particle density defined over a 3D Cartesian grid. The study features a methodology for evaluating the accuracy and performance of various density estimation methods, results of that evaluation for four density estimators, and a large-scale parallel algorithm for a self-adaptive method that computes a Voronoi tessellation as an intermediate step. We demonstrate the performance and scalability of our parallel algorithm on a supercomputer when estimating the density of 100 million particles over 500 billion grid points.Key words. Density estimation, cloud in cell, smoothed particle hydrodynamics, Voronoi tessellation, nearest grid point, triangular shaped clouds AMS subject classifications. 51-04, 68-04, 70-04, 85-04. 1. Introduction. In the context of scientific data analysis, density estimation is a transformation from discrete particle data to a continuous density function defined over a 3D field. This field can be interpolated, differentiated, and integrated: operations not possible in the particles' original discrete form. Moreover, the density field is discretized by a regular grid, which offers several advantages. (1) It is compact, in that x, y, z coordinates of the grid points are defined implicitly and need not be stored. (2) Applications using a regular grid are readily parallelizable because subdomain decomposition and processor assignment are also regular and implicit. (3) A regular grid is the most common data model for scientific data analysis and visualization algorithms. For example, most of the volume rendering literature in the past 25 years targets regular grids [9,24,37]; far less exists for adaptively refined grids [23] and unstructured meshes [30].Density estimation is a fundamental step needed whenever a discrete particle dataset is sampled over a continuous field. Our research is motivated by cosmology and astrophysics, but many other applications exist, for example estimating population density in geospatial applications or electron charge density in computational chemistry. Density estimation is also a key visualization step when an algorithm calls for a regular scalar field but particles are provided instead. For example, atom positions from molecular dynamics simulations may be converted to a grid before rendering an isosurface of atomic density.One of the earliest and arguably most popular density estimators, cloud in cell (CIC), was introduced 45 years ago [7]. Since then, smoothed particle hydrodynamics (SPH) and tessellation (TESS) methods have emerged. Combinations and variations also exist: for example, characteristics such as the window shape of CIC and the adaptivity of SPH can be combined. Nevertheless, two questions remain unanswered: Which density estimators are appropriate for a given problem domain and computational budget, and how can the estimator of choice be scaled to today's problem sizes? In this paper, we examine the first question by designi...

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The Accelerated Universe

Cited by 23 publications

References 4 publications

Dark Matter Halo Profiles of Massive Clusters: Theory Versus Observations

Dark Matter Halo Profiles of Massive Clusters: Theory Versus Observations

HACC: Simulating sky surveys on state-of-the-art supercomputing architectures

Self-Adaptive Density Estimation of Particle Data

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