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Šta je: nVidia TESLA superkompjuter

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joggurt
Milan Petrovic
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icon Šta je: nVidia TESLA superkompjuter19.09.2009. u 15:20 - pre 128 meseci
Može li neko u par rečenica da objasni šta ko radi u TESLA superkompjuteru!

Ako grafički procesori rade proracune koje inače radi glavni procesor CPU: Kako se zadaci distribuiraju na

grafičke procesore GPU, ako znamo da GPU pravi video i sliku, dali je u pitanju OS ili CUDA sistem ili nešto treće?

Pozz.
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nkrgovic
Nikola Krgović
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icon Re: Šta je: nVidia TESLA superkompjuter19.09.2009. u 16:20 - pre 128 meseci
Ukratko: Imas poseban programski jezik kojim programiras za te procesore. GPU-ovi ne prave "sliku", oni samo racunaju nesto vezano za sliku, ali to je vise-manje racun kao i svaki drugi. CUDA je alat koji ti omogucava da te procesore upotrebis za racun slican tom, ali da rezultat dobijes nazad u RAM-u.

Na primer: umesto da radis furijeove transformacije za obradu slike, radis ih za obradu podataka :D. Ista stvar, samo drugi cilj. Ne generises direktno sliku, vec nesto racunas. Na GPU mozes gledati kao na DSP optimizovan za odredjene algoritme, jer to je u sustini sve sto on i jeste. Ili na koprocesor za odrejen tip operacija, ako ti je lakse...
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joggurt
Milan Petrovic
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icon Re: Šta je: nVidia TESLA superkompjuter19.09.2009. u 18:24 - pre 128 meseci
Hvala!
Ko se jedanput opece i u jogurt duva!
 
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Majstor_Pućko

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icon Re: Šta je: nVidia TESLA superkompjuter19.09.2009. u 18:41 - pre 128 meseci
@nkrgovic
CUDA je u sustini kao neki driver sa API-jem koji pozivas iz obicnih prog jezika? Ili ja nisam dobro razumeo?
bolje je biti malo lud nego malo pametan
 
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nkrgovic
Nikola Krgović
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icon Re: Šta je: nVidia TESLA superkompjuter19.09.2009. u 19:10 - pre 128 meseci
Koliko sam ja shvatio postoji ceo jezik, koji lici na C. Nije samo drajver, mada mislim da i drajver mora biti optimizovan za to...

Mene je zapravo interesovalo samo nesto sto moze da koristi taj API/driver ne i samo programiranje. Uostalom, skini pa probaj? :D
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Catch 22

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icon Re: Šta je: nVidia TESLA superkompjuter19.09.2009. u 21:25 - pre 128 meseci
Nvidia Tesla
Citat:
The Tesla GPU is NVIDIA's third brand of GPUs. It is based on high-end GPUs from the G80 and on, as well as the Quadro lineup. Tesla is NVIDIA's first dedicated General Purpose GPU. The Tesla name originates from the inventor Nikola Tesla.
Tesla Overview
Because of the very high computational power (measured in floating point operations per second or FLOPS) compared to recent microprocessors, the Tesla products are intended for the high performance computing market. The lack of ability to output images to a display[1] is the main difference between Tesla products and ordinary video cards. The primary function of Tesla products are to aid in simulations, large scale calculations (especially floating-point calculations), and image generation for professional and scientific fields [2], with the use of CUDA.
...


CUDA (Compute Unified Device Architecture)

Citat:
CUDA (an acronym for Compute Unified Device Architecture) is a parallel computing architecture developed by NVIDIA. CUDA is the computing engine in NVIDIA graphics processing units or GPUs that is accessible to software developers through industry standard programming languages. Programmers use 'C for CUDA' (C with NVIDIA extensions), compiled through a PathScale Open64 C compiler,[1] to code algorithms for execution on the GPU. CUDA architecture supports a range of computational interfaces including OpenCL[2] and DirectCompute[3]. Third party wrappers are also available for Python, Fortran, Java and Matlab.

The latest drivers all contain the necessary CUDA components. CUDA works with all NVIDIA GPUs from the G8X series onwards, including GeForce, Quadro and the Tesla line. NVIDIA states that programs developed for the GeForce 8 series will also work without modification on all future Nvidia video cards, due to binary compatibility. CUDA gives developers access to the native instruction set and memory of the parallel computational elements in CUDA GPUs. Using CUDA, the latest NVIDIA GPUs effectively become open architectures like CPUs. Unlike CPUs however, GPUs have a parallel "many-core" architecture, each core capable of running thousands of threads simultaneously - if an application is suited to this kind of an architecture, the GPU can offer large performance benefits.

In the computer gaming industry, in addition to graphics rendering, graphics cards are used in game physics calculations (physical effects like debris, smoke, fire, fluids), examples include PhysX and Bullet. CUDA has also been used to accelerate non-graphical applications in computational biology, cryptography and other fields by an order of magnitude or more.[4][5][6][7] An example of this is the BOINC distributed computing client.[8]

CUDA provides both a low level API and a higher level API. The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0[9], which supersedes the beta released February 14, 2008

...

 
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