9,520 research outputs found
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A Component Architecture for High-Performance Scientific Computing
The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry
Robust Sparse Canonical Correlation Analysis
Canonical correlation analysis (CCA) is a multivariate statistical method
which describes the associations between two sets of variables. The objective
is to find linear combinations of the variables in each data set having maximal
correlation. This paper discusses a method for Robust Sparse CCA. Sparse
estimation produces canonical vectors with some of their elements estimated as
exactly zero. As such, their interpretability is improved. We also robustify
the method such that it can cope with outliers in the data. To estimate the
canonical vectors, we convert the CCA problem into an alternating regression
framework, and use the sparse Least Trimmed Squares estimator. We illustrate
the good performance of the Robust Sparse CCA method in several simulation
studies and two real data examples
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Leveraging legacy codes to distributed problem solving environments: A web service approach
This paper describes techniques used to leverage high performance legacy codes as CORBA components to a distributed problem solving environment. It first briefly introduces the software architecture adopted by the environment. Then it presents a CORBA oriented wrapper generator (COWG) which can be used to automatically wrap high performance legacy codes as CORBA components. Two legacy codes have been wrapped with COWG. One is an MPI-based molecular dynamic simulation (MDS) code, the other is a finite element based computational fluid dynamics (CFD) code for simulating incompressible Navier-Stokes flows. Performance comparisons between runs of the MDS CORBA component and the original MDS legacy code on a cluster of workstations and on a parallel computer are also presented. Wrapped as CORBA components, these legacy codes can be reused in a distributed computing environment. The first case shows that high performance can be maintained with the wrapped MDS component. The second case shows that a Web user can submit a task to the wrapped CFD component through a Web page without knowing the exact implementation of the component. In this way, a user’s desktop computing environment can be extended to a high performance computing environment using a cluster of workstations or a parallel computer
Correlated Component Analysis for diffuse component separation with error estimation on simulated Planck polarization data
We present a data analysis pipeline for CMB polarization experiments, running
from multi-frequency maps to the power spectra. We focus mainly on component
separation and, for the first time, we work out the covariance matrix
accounting for errors associated to the separation itself. This allows us to
propagate such errors and evaluate their contributions to the uncertainties on
the final products.The pipeline is optimized for intermediate and small scales,
but could be easily extended to lower multipoles. We exploit realistic
simulations of the sky, tailored for the Planck mission. The component
separation is achieved by exploiting the Correlated Component Analysis in the
harmonic domain, that we demonstrate to be superior to the real-space
application (Bonaldi et al. 2006). We present two techniques to estimate the
uncertainties on the spectral parameters of the separated components. The
component separation errors are then propagated by means of Monte Carlo
simulations to obtain the corresponding contributions to uncertainties on the
component maps and on the CMB power spectra. For the Planck polarization case
they are found to be subdominant compared to noise.Comment: 17 pages, accepted in MNRA
A D.C. Programming Approach to the Sparse Generalized Eigenvalue Problem
In this paper, we consider the sparse eigenvalue problem wherein the goal is
to obtain a sparse solution to the generalized eigenvalue problem. We achieve
this by constraining the cardinality of the solution to the generalized
eigenvalue problem and obtain sparse principal component analysis (PCA), sparse
canonical correlation analysis (CCA) and sparse Fisher discriminant analysis
(FDA) as special cases. Unlike the -norm approximation to the
cardinality constraint, which previous methods have used in the context of
sparse PCA, we propose a tighter approximation that is related to the negative
log-likelihood of a Student's t-distribution. The problem is then framed as a
d.c. (difference of convex functions) program and is solved as a sequence of
convex programs by invoking the majorization-minimization method. The resulting
algorithm is proved to exhibit \emph{global convergence} behavior, i.e., for
any random initialization, the sequence (subsequence) of iterates generated by
the algorithm converges to a stationary point of the d.c. program. The
performance of the algorithm is empirically demonstrated on both sparse PCA
(finding few relevant genes that explain as much variance as possible in a
high-dimensional gene dataset) and sparse CCA (cross-language document
retrieval and vocabulary selection for music retrieval) applications.Comment: 40 page
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