10,153 research outputs found
GADGET: A code for collisionless and gasdynamical cosmological simulations
We describe the newly written code GADGET which is suitable both for
cosmological simulations of structure formation and for the simulation of
interacting galaxies. GADGET evolves self-gravitating collisionless fluids with
the traditional N-body approach, and a collisional gas by smoothed particle
hydrodynamics. Along with the serial version of the code, we discuss a parallel
version that has been designed to run on massively parallel supercomputers with
distributed memory. While both versions use a tree algorithm to compute
gravitational forces, the serial version of GADGET can optionally employ the
special-purpose hardware GRAPE instead of the tree. Periodic boundary
conditions are supported by means of an Ewald summation technique. The code
uses individual and adaptive timesteps for all particles, and it combines this
with a scheme for dynamic tree updates. Due to its Lagrangian nature, GADGET
thus allows a very large dynamic range to be bridged, both in space and time.
So far, GADGET has been successfully used to run simulations with up to 7.5e7
particles, including cosmological studies of large-scale structure formation,
high-resolution simulations of the formation of clusters of galaxies, as well
as workstation-sized problems of interacting galaxies. In this study, we detail
the numerical algorithms employed, and show various tests of the code. We
publically release both the serial and the massively parallel version of the
code.Comment: 32 pages, 14 figures, replaced to match published version in New
Astronomy. For download of the code, see
http://www.mpa-garching.mpg.de/gadget (new version 1.1 available
Nature-Inspired Learning Models
Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge
from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new
learning methods has been found in the mechanics of physical fields found in both micro and macro scale.
Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the
field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over
the well-known real and artificial datasets, compared when possible to the traditional methods
A Parallel Adaptive P3M code with Hierarchical Particle Reordering
We discuss the design and implementation of HYDRA_OMP a parallel
implementation of the Smoothed Particle Hydrodynamics-Adaptive P3M (SPH-AP3M)
code HYDRA. The code is designed primarily for conducting cosmological
hydrodynamic simulations and is written in Fortran77+OpenMP. A number of
optimizations for RISC processors and SMP-NUMA architectures have been
implemented, the most important optimization being hierarchical reordering of
particles within chaining cells, which greatly improves data locality thereby
removing the cache misses typically associated with linked lists. Parallel
scaling is good, with a minimum parallel scaling of 73% achieved on 32 nodes
for a variety of modern SMP architectures. We give performance data in terms of
the number of particle updates per second, which is a more useful performance
metric than raw MFlops. A basic version of the code will be made available to
the community in the near future.Comment: 34 pages, 12 figures, accepted for publication in Computer Physics
Communication
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