Šta je: nVidia TESLA superkompjuter

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.
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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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