257,922 research outputs found
Parallel HOP: A Scalable Halo Finder for Massive Cosmological Data Sets
Modern N-body cosmological simulations contain billions () of dark
matter particles. These simulations require hundreds to thousands of gigabytes
of memory, and employ hundreds to tens of thousands of processing cores on many
compute nodes. In order to study the distribution of dark matter in a
cosmological simulation, the dark matter halos must be identified using a halo
finder, which establishes the halo membership of every particle in the
simulation. The resources required for halo finding are similar to the
requirements for the simulation itself. In particular, simulations have become
too extensive to use commonly-employed halo finders, such that the
computational requirements to identify halos must now be spread across multiple
nodes and cores. Here we present a scalable-parallel halo finding method called
Parallel HOP for large-scale cosmological simulation data. Based on the halo
finder HOP, it utilizes MPI and domain decomposition to distribute the halo
finding workload across multiple compute nodes, enabling analysis of much
larger datasets than is possible with the strictly serial or previous parallel
implementations of HOP. We provide a reference implementation of this method as
a part of the toolkit yt, an analysis toolkit for Adaptive Mesh Refinement
(AMR) data that includes complementary analysis modules. Additionally, we
discuss a suite of benchmarks that demonstrate that this method scales well up
to several hundred tasks and datasets in excess of particles. The
Parallel HOP method and our implementation can be readily applied to any kind
of N-body simulation data and is therefore widely applicable.Comment: 29 pages, 11 figures, 2 table
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Comparison of Current Gravity Estimation and Determination Models
This paper will discuss the history of gravity estimation and determination models while analyzing methods that are in development. Some fundamental methods for calculating the gravity field include spherical harmonics solutions, local weighted interpolation, and global point mascon modeling (PMC). Recently, high accuracy measurements have become more accessible, and the requirements for high order geopotential modeling have become more stringent. Interest in irregular bodies, accurate models of the hydrological system, and on-board processing has demanded a comprehensive model that can quickly and accurately compute the geopotential with low memory costs. This trade study of current geopotential modeling techniques will reveal that each modeling technique has a unique use case. It is notable that the spherical harmonics model is relatively accurate but poses a cumbersome inversion problem. PMC and interpolation models, on the other hand, are computationally efficient, but require more research to become robust models with high levels of accuracy. Considerations of the trade study will suggest further research for the point mascon model. The PMC model should be improved through mascon refinement, direct solutions that stem from geodetic measurements, and further validation of the gravity gradient. Finally, the potential for each model to be implemented with parallel computation will be shown to lead to large improvements in computing time while reducing the memory cost for each technique.Aerospace Engineering and Engineering Mechanic
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