3,367 research outputs found
Computational Particle Physics for Event Generators and Data Analysis
High-energy physics data analysis relies heavily on the comparison between
experimental and simulated data as stressed lately by the Higgs search at LHC
and the recent identification of a Higgs-like new boson. The first link in the
full simulation chain is the event generation both for background and for
expected signals. Nowadays event generators are based on the automatic
computation of matrix element or amplitude for each process of interest.
Moreover, recent analysis techniques based on the matrix element likelihood
method assign probabilities for every event to belong to any of a given set of
possible processes. This method originally used for the top mass measurement,
although computing intensive, has shown its power at LHC to extract the new
boson signal from the background.
Serving both needs, the automatic calculation of matrix element is therefore
more than ever of prime importance for particle physics. Initiated in the
eighties, the techniques have matured for the lowest order calculations
(tree-level), but become complex and CPU time consuming when higher order
calculations involving loop diagrams are necessary like for QCD processes at
LHC. New calculation techniques for next-to-leading order (NLO) have surfaced
making possible the generation of processes with many final state particles (up
to 6). If NLO calculations are in many cases under control, although not yet
fully automatic, even higher precision calculations involving processes at
2-loops or more remain a big challenge.
After a short introduction to particle physics and to the related theoretical
framework, we will review some of the computing techniques that have been
developed to make these calculations automatic. The main available packages and
some of the most important applications for simulation and data analysis, in
particular at LHC will also be summarized.Comment: 19 pages, 11 figures, Proceedings of CCP (Conference on Computational
Physics) Oct. 2012, Osaka (Japan) in IOP Journal of Physics: Conference
Serie
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate
high performance code for multiple platforms including multicores, GPUs, and
distributed machines. Tiramisu introduces a scheduling language with novel
extensions to explicitly manage the complexities that arise when targeting
these systems. The framework is designed for the areas of image processing,
stencils, linear algebra and deep learning. Tiramisu has two main features: it
relies on a flexible representation based on the polyhedral model and it has a
rich scheduling language allowing fine-grained control of optimizations.
Tiramisu uses a four-level intermediate representation that allows full
separation between the algorithms, loop transformations, data layouts, and
communication. This separation simplifies targeting multiple hardware
architectures with the same algorithm. We evaluate Tiramisu by writing a set of
image processing, deep learning, and linear algebra benchmarks and compare them
with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu
matches or outperforms existing compilers and libraries on different hardware
architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041
ELSI: A Unified Software Interface for Kohn-Sham Electronic Structure Solvers
Solving the electronic structure from a generalized or standard eigenproblem
is often the bottleneck in large scale calculations based on Kohn-Sham
density-functional theory. This problem must be addressed by essentially all
current electronic structure codes, based on similar matrix expressions, and by
high-performance computation. We here present a unified software interface,
ELSI, to access different strategies that address the Kohn-Sham eigenvalue
problem. Currently supported algorithms include the dense generalized
eigensolver library ELPA, the orbital minimization method implemented in
libOMM, and the pole expansion and selected inversion (PEXSI) approach with
lower computational complexity for semilocal density functionals. The ELSI
interface aims to simplify the implementation and optimal use of the different
strategies, by offering (a) a unified software framework designed for the
electronic structure solvers in Kohn-Sham density-functional theory; (b)
reasonable default parameters for a chosen solver; (c) automatic conversion
between input and internal working matrix formats, and in the future (d)
recommendation of the optimal solver depending on the specific problem.
Comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800
basis functions) on distributed memory supercomputing architectures.Comment: 55 pages, 14 figures, 2 table
A differential algebra based importance sampling method for impact probability computation on Earth resonant returns of Near Earth Objects
A differential algebra based importance sampling method for uncertainty
propagation and impact probability computation on the first resonant returns of
Near Earth Objects is presented in this paper. Starting from the results of an
orbit determination process, we use a differential algebra based automatic
domain pruning to estimate resonances and automatically propagate in time the
regions of the initial uncertainty set that include the resonant return of
interest. The result is a list of polynomial state vectors, each mapping
specific regions of the uncertainty set from the observation epoch to the
resonant return. Then, we employ a Monte Carlo importance sampling technique on
the generated subsets for impact probability computation. We assess the
performance of the proposed approach on the case of asteroid (99942) Apophis. A
sensitivity analysis on the main parameters of the technique is carried out,
providing guidelines for their selection. We finally compare the results of the
proposed method to standard and advanced orbital sampling techniques
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
Long term nonlinear propagation of uncertainties in perturbed geocentric dynamics using automatic domain splitting
Current approaches to uncertainty propagation in astrodynamics mainly refer tolinearized models or Monte Carlo simulations. Naive linear methods fail in nonlinear dynamics, whereas Monte Carlo simulations tend to be computationallyintensive. Differential algebra has already proven to be an efficient compromiseby replacing thousands of pointwise integrations of Monte Carlo runs with thefast evaluation of the arbitrary order Taylor expansion of the flow of the dynamics. However, the current implementation of the DA-based high-order uncertainty propagator fails in highly nonlinear dynamics or long term propagation. We solve this issue by introducing automatic domain splitting. During propagation, the polynomial of the current state is split in two polynomials when its accuracy reaches a given threshold. The resulting polynomials accurately track uncertainties, even in highly nonlinear dynamics and long term propagations. Furthermore, valuable additional information about the dynamical system is available from the pattern in which those automatic splits occur. From this pattern it is immediately visible where the system behaves chaotically and where its evolution is smooth. Furthermore, it is possible to deduce the behavior of the system for each region, yielding further insight into the dynamics. In this work, the method is applied to the analysis of an end-of-life disposal trajectory of the INTEGRAL spacecraft
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