Ola,
Sim, as GPU estão a ser cada vez mais usadas para computação científica,
embora não seja a minha especialidade deixo aqui algumas referencias
interessantes na web (talvez ajude quem não conhece o tópico):
Extracto da Wikipedia:
http://en.wikipedia.org/wiki/Graphics_processing_unit
"A new concept is to use a general purpose graphics processing unit
<http://en.wikipedia.org/wiki/GPGPU> as a modified form of stream
processor <http://en.wikipedia.org/wiki/Stream_processing>. This concept
turns the massive floating-point
<http://en.wikipedia.org/wiki/Floating-point> computational power of a
modern graphics accelerator's shader pipeline into general-purpose
computing power, as opposed to being hard wired solely to do graphical
operations. In certain applications requiring massive vector operations,
this can yield several orders of magnitude higher performance than a
conventional CPU. The two largest discrete (see "Dedicated graphics
cards" above) GPU designers, ATI
<http://en.wikipedia.org/wiki/ATI_Technologies> and NVIDIA
<http://en.wikipedia.org/wiki/NVIDIA>, are beginning to pursue this new
approach with an array of applications. Both nVidia and ATI have teamed
with Stanford University
<http://en.wikipedia.org/wiki/Stanford_University> to create a GPU-based
client for the fold...@home <http://en.wikipedia.org/wiki/fold...@home>
distributed computing project, for protein folding calculations. In
certain circumstances the GPU calculates forty times faster than the
conventional CPUs traditionally used by such applications.^[10]
<http://en.wikipedia.org/wiki/Graphics_processing_unit#cite_note-9>
^[11] <http://en.wikipedia.org/wiki/Graphics_processing_unit#cite_note-10>
Recently NVidia began releasing cards supporting an API extension to the
C <http://en.wikipedia.org/wiki/C_%28programming_language%29>
programming language CUDA <http://en.wikipedia.org/wiki/CUDA> ("Compute
Unified Device Architecture"), which allows specified functions from a
normal C program to run on the GPU's stream processors. This makes C
programs capable of taking advantage of a GPU's ability to operate on
large matrices in parallel, while still making use of the CPU when
appropriate. CUDA is also the first API to allow CPU-based applications
to access directly the resources of a GPU for more general purpose
computing without the limitations of using a graphics API."
Entretanto um SIG que já utiliza o CUDA (programação em cima das GPU) é
o Manifold:
http://www.manifold.net/index.shtml
"Using the optional *Manifold Surface Tools*
<http://www.manifold.net/info/surface_tools.shtml> extension, Manifold
Release 8 is the first GIS ever to support *massively parallel*
computing using *hundreds* of stream processors via NVIDIA® CUDA^(TM)
technology. By installing an NVIDIA GPGPU card (widely available for as
little as $150 per card with up to 256 processors per card) you can add
true supercomputing performance to your GIS installation. Manifold
automatically recognizes and utilizes up to four NVIDIA CUDA cards for
up to *1024 processors* with teraflops of computational performance.
Dozens of functions can be run by Manifold at supercomputer speeds with
an NVIDIA CUDA-capable card installed."
Um comparativo do "Tom's Hardware" a falar das vantagens do uso das GPu
(embora não veja SIG por lá):
http://www.tomshardware.com/reviews/nvidia-cuda-gpgpu,2299.html
Portal que congrega informação sobre o assunto (boa referência):
http://gpgpu.org/
A ATI (que agora é da AMD - para os mais distraídos...) tambem tem já
possibilidades semelhantes ao CUDA da NVIDIA e o OPENCL promete dar
acesso ao hardware de todos o sfabricantes:
"The move to open standards with the new Heterogeneous Computing
Initiative
<http://www.khronos.org/news/press/releases/khronos_launches_heterogeneous_computing_initiative/>
supporting OpenCL <http://en.wikipedia.org/wiki/OpenCL> (Open Computing
Language), will be a good move for both AMD and NVIDIA. The idea is
that an application developer would write an OpenCL-based stream
computing application, and it would run on any GPU or CPU with an OpenCL
driver. Both AMD and Nvidia have indicated they want to support this new
standard."
http://fireuser.com/blog/amd_stream_computing_and_nvidia_cuda_similar_but_different/
Parece que a vantagem do OpenCL será a de usar o processador que se
quiser (ou todos?)
"- OpenCL platform was created to define a general, open standard of
GPGPU acces and usage. It also enabled to use different GPGPU-able
devices to work parallel each other. Like if you have a big machine with
2 processors and 3 videocards, the OpenCL device list will provide you
handles for 5 devices (3 GPUs and 2CPUs) and you can select on which you
want to run your code on. (While CUDA was not this flexible)"
Não tenho notado conversas sobre isto nos projectos SIG open source, mas
acho que já será possível fazer uzo destas tecnologias? Ai sim
poderiamos ver o poder destas placas a chegar rapidamente a muitos :-)
Victor Ferreira
Pedro Matos wrote:
A questao das placas graficas originalmente desenhadas para jogos
aplicadas noutras areas é muito interessante. Nalgumas aplicacoes, os
militares americanos e ingleses (pelo menos) estao a desistir dos
tradicionais contratos de desenvolvimento de hardware dedicado por o
sector comercial ser imbativel.
O pentagono acabou de comprar 2000 processadores xbox para montar um
super parallel computer, e os ingleses estimam que uma placa NVIDIA de
300 libras substitui equipamento militar no valor de 30.000 libras. (a
referencia é do Economist)
É tempo de comecarmos tambem nos a prestar tanta atencao as nossas
placas graficas como a que damos ao software. Acabei de trocar o meu
computador standard por uma maquina mais potente com uma placa grafica
decente e nalgumas operacoes ArcGIS os ganhos de tempo melhoraram 10
vezes.
Pedro
Top Story
Why Geospatial Users and Developers Should Know Their GPU from their
CPU
<http://newsletter.directionsmag.com/link.php?M=74078&N=2504&L=30331>*Why
Geospatial Users and Developers Should Know Their GPU from their CPU
<http://newsletter.directionsmag.com/link.php?M=74078&N=2504&L=30331>*
The buzz about advances in geospatial software has overshadowed that
of hardware for the last five to ten years. But Executive Editor Adena
Schutzberg suggests that perhaps we should pay more attention to
hardware, especially since the graphics processing unit (GPU) may be
one of our best weapons to increase productivity.
http://www.directionsmag.com/article.php?article_id=3418
<http://www.directionsmag.com/article.php?article_id=3418>
_______________________________________________
Portugal mailing list
[email protected]
http://lists.osgeo.org/mailman/listinfo/portugal
