32,223 research outputs found
MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data
In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms
GPU acceleration of brain image proccessing
Durante los últimos años se ha venido demostrando el alto poder computacional
que ofrecen las GPUs a la hora de resolver determinados problemas.
Al mismo tiempo, existen campos en los que no es posible beneficiarse completamente
de las mejoras conseguidas por los investigadores, debido principalmente
a que los tiempos de ejecución de las aplicaciones llegan a ser extremadamente
largos. Este es por ejemplo el caso del registro de imágenes en medicina.
A pesar de que se han conseguido aceleraciones sobre el registro de imágenes,
su uso en la práctica clÃnica es aún limitado. Entre otras cosas, esto se debe
al rendimiento conseguido.
Por lo tanto se plantea como objetivo de este proyecto, conseguir mejorar los
tiempos de ejecución de una aplicación dedicada al resgitro de imágenes en medicina,
con el fin de ayudar a aliviar este problema
The LCG POOL Project, General Overview and Project Structure
The POOL project has been created to implement a common persistency framework
for the LHC Computing Grid (LCG) application area. POOL is tasked to store
experiment data and meta data in the multi Petabyte area in a distributed and
grid enabled way. First production use of new framework is expected for summer
2003. The project follows a hybrid approach combining C++ Object streaming
technology such as ROOT I/O for the bulk data with a transactionally safe
relational database (RDBMS) store such as MySQL. POOL is based a strict
component approach - as laid down in the LCG persistency and blue print RTAG
documents - providing navigational access to distributed data without exposing
details of the particular storage technology. This contribution describes the
project breakdown into work packages, the high level interaction between the
main pool components and summarizes current status and plans.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 5 pages. PSN MOKT00
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