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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
Ö Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
A Wavelet Parallel Code for Structure Detection
A. Pagliaro
Istituto di Astronomia dell'Universit‘a di Catania
U. Becciani, V.Antonuccio and M.Gambera
Osservatorio Astrofisico di Catania
Abstract. We describe a parallel code for 3­D structure detection and
morphological analysis. The method is based on a multiscale technique,
the wavelet transform and on segmentation analysis. The wavelet trans­
form allows us to find substructures at di#erent scales and the segmenta­
tion method allows us to make a quantitative analysis of them. The code
is based on the message passing programming paradigm and major speed
inprovements are achieved by using a strategy of domain decomposition.
1. Introduction
This paper presents a new parallel code, that allows a rapid structure de­
tection and morphological analysis in a 3­D set of data points. The code is
described in greater detail in Becciani & Pagliaro, 1997. A serial version of this
code has been successfully used in the analysis of the Coma cluster (Gambera
et al., 1997). However, possible applications of this code are not limited to
astrophysics but may also benefit several other fields of science.
Our method of structure detection is based on the wavelet transform (see
Grossmann & Morlet 1984, 1987) evaluated at several scales and on segmentation
analysis (see also Escalera & Mazure 1992, Lega 1994, Lega et al. 1995).
2. Method Overview
The detection method can be divided into three main steps.
1. Computation of the wavelet matrices on all the scales investigated. These are
computed, by means of the ``’a trous'' algorithm, for the data to be analyzed and
on a random distribution in the same region of space and on the same grid as
the real data. On these latter matrices we calculate the threshold corresponding
to a fixed confidence level in the structure detection.
2. Segmentation analysis. The aim of this analysis is to have all the con­
nected pixels with a wavelet coe#cients greater than the threshold labeled with
an integer number di#erent for every single structure.
493

494 Pagliaro, Becciani, Antonuccio and Gambera
3. Computation of a morphological parameter for every structure singled out
and of a mean morphological parameter for each scale.
3. The Implementation
Our strategy has been to develop a parallel code that can run both on
multiprocessors or MPP systems and on clusters of workstations; the latter are
easily available at low cost without a specific investment in supercomputing.
Hence, we have adopted a message passing technique and subdivided the
computational domain into subdomains, assigning the wavelet and segmentation
analysis of the subdomain to di#erent processors: each processor executes in a
parallel way the analysis with memory usage and computational load decreasing
as the number of working processors grow. For the development of our code we
have choose to use PVM (Parallel Virtual Machine), a set of C and fortran
function written by the OACK Ridge National Laboratory.
4. The Parallel Code
The computational domain is made of the region of space that comprises all
the data points from the simulation or the catalogue to be analyzed. Recogni­
tion of the substructures happens by means of the wavelet coe#cient computa­
tion and by the segmentation analysis. Our code is based on the programming
paradigm MASTER/SLAVE. The computational domain is subdivided into
M subdomains (proportionally to the weights of the hosts), along a split axis
chosen as the longest one. Each subdomain is assigned to a host of the virtual
machine. On the wave slaves both a fault tolerance mechanism and a dynami­
cal load balance one have been implemented. All the quantities computed are
written on a shared area of the disk, accessible to all the tasks. This is useful
for an e#cient fault tolerance mechanism.
The label slaves are spawned at the end of the previous jobs. Each slave
executes the segmentation analysis on the subdomain assigned to it by the mas­
ter and recognizes and numbers the substructures inside it. The labels found by
each slave are written on a shared area.
The last part of the code is executed only by the master, that reads the
results written on the shared area by the slaves and rearranges them. The master
reads the labels that each slaves has assigned to the substructures found in its
subdomain and makes an analysis of the borders between the subdomains. The
task is to recognize those structures that di#erent adjacent hosts have singled
out and that are really only one substructure across the border. Finally, the
morphological analysis is executed by the master.
References
Becciani, U., Pagliaro, A., Comp. Phys. Comms, submitted
Escalera, E., Mazure, A., 1992, ApJ, 388, 23

A Wavelet Parallel Code for Structure Detection 495
Gambera, M., Pagliaro, A., Antonuccio­Delogu, V., Becciani, U., 1997, ApJ,
488, 136
Grossmann, A., Morlet, J., 1984, SIAM J. Math., 15, 723
Grossmann, A., Morlet, J., 1987, Math. & Phys., Lectures on recent results, ed.
L.Streit, World Scientific
Lega, E., 1994, These de Doctorat, Universit’e de Nice (L94)
Lega, E., Scholl, H., Alimi, J.­M., Bijaoui, A., Bury, P., 1995, Parallel Comput­
ing, 21, 265