23,576 research outputs found
Towards a Mini-App for Smoothed Particle Hydrodynamics at Exascale
The smoothed particle hydrodynamics (SPH) technique is a purely Lagrangian
method, used in numerical simulations of fluids in astrophysics and
computational fluid dynamics, among many other fields. SPH simulations with
detailed physics represent computationally-demanding calculations. The
parallelization of SPH codes is not trivial due to the absence of a structured
grid. Additionally, the performance of the SPH codes can be, in general,
adversely impacted by several factors, such as multiple time-stepping,
long-range interactions, and/or boundary conditions. This work presents
insights into the current performance and functionalities of three SPH codes:
SPHYNX, ChaNGa, and SPH-flow. These codes are the starting point of an
interdisciplinary co-design project, SPH-EXA, for the development of an
Exascale-ready SPH mini-app. To gain such insights, a rotating square patch
test was implemented as a common test simulation for the three SPH codes and
analyzed on two modern HPC systems. Furthermore, to stress the differences with
the codes stemming from the astrophysics community (SPHYNX and ChaNGa), an
additional test case, the Evrard collapse, has also been carried out. This work
extrapolates the common basic SPH features in the three codes for the purpose
of consolidating them into a pure-SPH, Exascale-ready, optimized, mini-app.
Moreover, the outcome of this serves as direct feedback to the parent codes, to
improve their performance and overall scalability.Comment: 18 pages, 4 figures, 5 tables, 2018 IEEE International Conference on
Cluster Computing proceedings for WRAp1
SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App
Numerical simulations of fluids in astrophysics and computational fluid
dynamics (CFD) are among the most computationally-demanding calculations, in
terms of sustained floating-point operations per second, or FLOP/s. It is
expected that these numerical simulations will significantly benefit from the
future Exascale computing infrastructures, that will perform 10^18 FLOP/s. The
performance of the SPH codes is, in general, adversely impacted by several
factors, such as multiple time-stepping, long-range interactions, and/or
boundary conditions. In this work an extensive study of three SPH
implementations SPHYNX, ChaNGa, and XXX is performed, to gain insights and to
expose any limitations and characteristics of the codes. These codes are the
starting point of an interdisciplinary co-design project, SPH-EXA, for the
development of an Exascale-ready SPH mini-app. We implemented a rotating square
patch as a joint test simulation for the three SPH codes and analyzed their
performance on a modern HPC system, Piz Daint. The performance profiling and
scalability analysis conducted on the three parent codes allowed to expose
their performance issues, such as load imbalance, both in MPI and OpenMP.
Two-level load balancing has been successfully applied to SPHYNX to overcome
its load imbalance. The performance analysis shapes and drives the design of
the SPH-EXA mini-app towards the use of efficient parallelization methods,
fault-tolerance mechanisms, and load balancing approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1809.0801
WSClean : an implementation of a fast, generic wide-field imager for radio astronomy
This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society. © 2014 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.Astronomical widefield imaging of interferometric radio data is computationally expensive, especially for the large data volumes created by modern non-coplanar many-element arrays. We present a new widefield interferometric imager that uses the w-stacking algorithm and can make use of the w-snapshot algorithm. The performance dependencies of CASA's w-projection and our new imager are analysed and analytical functions are derived that describe the required computing cost for both imagers. On data from the Murchison Widefield Array, we find our new method to be an order of magnitude faster than w-projection, as well as being capable of full-sky imaging at full resolution and with correct polarisation correction. We predict the computing costs for several other arrays and estimate that our imager is a factor of 2-12 faster, depending on the array configuration. We estimate the computing cost for imaging the low-frequency Square-Kilometre Array observations to be 60 PetaFLOPS with current techniques. We find that combining w-stacking with the w-snapshot algorithm does not significantly improve computing requirements over pure w-stacking. The source code of our new imager is publicly released.Peer reviewedFinal Published versio
Fast Point Spread Function Modeling with Deep Learning
Modeling the Point Spread Function (PSF) of wide-field surveys is vital for
many astrophysical applications and cosmological probes including weak
gravitational lensing. The PSF smears the image of any recorded object and
therefore needs to be taken into account when inferring properties of galaxies
from astronomical images. In the case of cosmic shear, the PSF is one of the
dominant sources of systematic errors and must be treated carefully to avoid
biases in cosmological parameters. Recently, forward modeling approaches to
calibrate shear measurements within the Monte-Carlo Control Loops ()
framework have been developed. These methods typically require simulating a
large amount of wide-field images, thus, the simulations need to be very fast
yet have realistic properties in key features such as the PSF pattern. Hence,
such forward modeling approaches require a very flexible PSF model, which is
quick to evaluate and whose parameters can be estimated reliably from survey
data. We present a PSF model that meets these requirements based on a fast
deep-learning method to estimate its free parameters. We demonstrate our
approach on publicly available SDSS data. We extract the most important
features of the SDSS sample via principal component analysis. Next, we
construct our model based on perturbations of a fixed base profile, ensuring
that it captures these features. We then train a Convolutional Neural Network
to estimate the free parameters of the model from noisy images of the PSF. This
allows us to render a model image of each star, which we compare to the SDSS
stars to evaluate the performance of our method. We find that our approach is
able to accurately reproduce the SDSS PSF at the pixel level, which, due to the
speed of both the model evaluation and the parameter estimation, offers good
prospects for incorporating our method into the framework.Comment: 25 pages, 8 figures, 1 tabl
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