[Noti-TC] Invitación a presenciar una defensa de Tesis

[jdelia at intec.unl.edu.ar]

    * [24-06-08, 12:00] Invitación a presenciar una defensa de Tesis.
       * Día y lugar: *Jueves 26/6/2008, 16:30hs*, en la *Sala de  
Conferencias Prof. Juan Carlos Alarcón* de la FICH.
       * Tesis de Doctorado en Ingeniería de la UNL-FICH, mención  
Mecánica Computacional.
       * Título: "Techniques for High-Performace Distributed Computing  
in Computational Fluid Mechanics". Tesista  Lisandro Dalcín y director  
Mario Storti (profesores de la materia *Algoritmos y Estructuras de  
Datos*).
       * Abstract: "Although a lot of progress has been made in theory  
as well as practice, the true costs of accessing parallel environments  
are still largely dominated by software.  The number of end-user  
parallelized applications is still very small, as well as the number  
of people affected to their development. Engineers and scientists not  
specialized in programming or numerical computing, and even small and  
medium size software companies, hardly ever considered developing  
their own parallelized code. High performance computing is  
traditionally associated with software development using compiled  
languages. However, in typical applications programs, only a small  
part of the code is time-critical enough to require the efficiency of  
compiled languages. The rest of the code is generally related to  
memory management, error handling, input/output, and user interaction,  
and those are usually the most error-prone and time-consuming lines of  
code to write and debug in the whole development process. Interpreted  
high-level languages can be really advantageous for these kind of  
tasks. This thesis reports the attempts to facilitate the access to  
high-performance parallel computing resources within a Python  
programming environment. The target audience are all members of the  
scientific and engineering community using Python on a regular basis  
as the supporting environment for developing applications and  
performing numerical simulations. The target computing platforms range  
from multiple-processor and/or multiple-core desktop computers,  
clusters of workstations or dedicated computing nodes either with  
standard or special network interconnects, to high-performance shared  
memory machines. The net result of this effort are two open source and  
public domain packages, MPI for Python (known in short as mpi4py) and  
PETSc for Python (known in short as petsc4py). MPI for Python, is an  
open-source, public-domain software project that provides bindings of  
the Message Passing Interface (MPI) standard for the Python  
programming language. MPI for Python is a general-purpose and  
full-featured package targeting the development of parallel  
application codes in Python. Its facilities allow parallel Python  
programs to easily exploit multiple processors. MPI for Python employs  
a back-end MPI implementation, thus being immediately available on any  
parallel environment providing access to any MPI library. PETSc for  
Python is an open-source, public-domain software project that provides  
access to the Portable, Extensible Toolkit for Scientific Computation  
(PETSc) libraries within the Python programming language. PETSc for  
Python is a general-purpose and full-featured package. Its facilities  
allow sequential and parallel Python applications to exploit state of  
the art algorithms and data structures readily available in PETSc. MPI  
for Python and PETSc for Python packages are fully integrated to  
PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite  
elements code. Within a parallel Python programming environment, this  
software infrastructure supported research activities related to the  
simulation of electrophoretic processes in microfluidic chips. This  
work is part of a multidisciplinary effort oriented to design and  
develop these devices in order to improve current techniques in  
clinical analysis and early diagnosis of cancer".
-- 




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