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NVIDIA GPU Computing & CUDA FAQ E-mail
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Written by Olin Coles and NVIDIA   
Monday, 16 June 2008
Table of Contents: Page Index
NVIDIA GPU Computing & CUDA FAQ
Compute Unified Device Architecture
Parallel Computing with GeForce
GPU vs. CPU Architecture
GPU Computing Performance

CUDA: Compute Unified Device Architecture

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What is CUDA?

NVIDIA CUDA (Compute Unified Device Architecture) technology is the world's only C language environment that enables programmers and developers to write software to solve complex computational problems in a fraction of the time by tapping into the many-core parallel processing power of GPUs. With millions of CUDA-capable GPUs already deployed, thousands of software programmers are already using the free CUDA software tools to accelerate applications-from video and audio encoding to oil and gas exploration, product design, medical imaging, and scientific research.

Providing orders of magnitude more performance than current CPUs and simplifying software development by extending the standard C language, CUDA technology enables developers to create innovative solutions for data-intensive problems. For advanced research and language development, CUDA includes a low level assembly language layer and driver interface.

CUDA is a software and GPU architecture that makes it possible to use the many processor cores (and eventually thousands of cores) in a GPU to perform general-purpose mathematical calculations. CUDA is accessible to all programmers through an extension to the C and C++ programming languages for parallel computing.

Technology Features:

  • Standard C language for parallel application development on the GPU
  • Standard numerical libraries for FFT (Fast Fourier Transform) and BLAS (Basic Linear Algebra Subroutines)
  • Dedicated CUDA driver for computing with fast data transfer path between GPU and CPU
  • CUDA driver interoperates with OpenGL and DirectX graphics drivers
  • Support for Linux 32/64-bit and Windows XP 32/64-bit operating systems

Does CUDA also work on the CPU?

Yes! The upcoming version of CUDA will support multicore CPUs. See the CUDA FAQ for more details.

How is CUDA different from GPGPU?

CUDA is designed from the ground-up for efficient general purpose computation on GPUs. It uses a C-like programming language and does not require remapping algorithms to graphics concepts. CUDA is an extension to C for parallel computing. It allows the programmer to program in C, without the need to translate problems into graphics concepts. Anyone who can program C can swiftly learn to program in CUDA.

GPGPU (General-Purpose computation on GPUs) uses graphics APIs like DirectX and OpenGL for computation. It requires detailed knowledge of graphics APIs and hardware. The programming model is limited in terms of random read and write and thread cooperation.

CUDA exposes several hardware features that are not available via the graphics API. The most significant of these is shared memory, which is a small (currently 16KB per multiprocessor) area of on-chip memory which can be accessed in parallel by blocks of threads. This allows caching of frequently used data and can provide large speedups over using textures to access data. Combined with a thread synchronization primitive, this allows cooperative parallel processing of on-chip data, greatly reducing the expensive off-chip bandwidth requirements of many parallel algorithms. This benefits a number of common applications such as linear algebra, Fast Fourier Transforms, and image processing filters.

Whereas fragment programs in the graphics API are limited to outputting 32 floats (RGBA * 8 render targets) at a pre-specified location, CUDA supports scattered writes - i.e. an unlimited number of stores to any address. This enables many new algorithms that were not possible to perform efficiently using graphics-based GPGPU.

The graphics API forces the user to store data in textures, which requires packing long arrays into 2D textures. This is cumbersome and imposes extra addressing math. CUDA can perform loads from any address. CUDA also offers highly optimized data transfers to and from the GPU.

CUDA is available free from NVIDIA at the CUDA Zone website.



 

Comments 

 
# cudahamid 2010-09-26 04:03
hello
can i program gtx 260 with visual studio ?
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# RE: cudaOlin Coles 2010-09-26 08:04
Yes. The GeForce GTX 260 is fully compatible with CUDA, and will work with Visual Studio.
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