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NVIDIA CUDA
Revolutionary GPU Computing
NVIDIA® CUDA™ technology is a fundamentally new computing architecture that enables the GPU to solve complex computational problems in consumer, business, and technical applications. CUDA (Compute Unified Device Architecture) technology gives computationally intensive applications access to the tremendous processing power of NVIDIA graphics processing units (GPUs) through a revolutionary new programming interface. Providing orders of magnitude more performance and simplifying software development by using 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.
Developing with CUDA
The CUDA Toolkit is a complete software development solution for programming CUDA-enabled GPUs. The Toolkit includes standard FFT and BLAS libraries, a C-compiler for the NVIDIA GPU and a runtime driver. The CUDA runtime driver is a separate standalone driver that interoperates with OpenGL and Microsoft® DirectX® drivers from NVIDIA. CUDA technology is currently supported on the Linux and Microsoft® Windows® XP operating systems.
The CUDA Developer SDK provides examples with source code to help you get started with CUDA. Examples include:
* Parallel bitonic sort
* Matrix multiplication
* Matrix transpose
* Performance profiling using timers
* Parallel prefix sum (scan) of large arrays
* Image convolution
* 1D DWT using Haar wavelet
* OpenGL and Direct3D graphics interoperation examples
* CUDA BLAS and FFT library usage examples
* CPU-GPU C- and C++-code integration
Technology Features
* Unified hardware and software solution for parallel computing on CUDA-enabled NVIDIA GPUs
* CUDA-enabled GPUs support the Parallel Data Cache and Thread Execution Manager for high performance computing
* Standard C programming language enabled on a GPU
* Standard numerical libraries for FFT (Fast Fourier Transform) and BLAS (Basic Linear Algebra Subroutines)
* Dedicated CUDA driver for computing
* Optimized upload and download path from the CPU to CUDA-enabled GPU
* CUDA driver interoperates with OpenGL and DirectX graphics drivers
* Support for Linux and Windows XP operating systems
* Scales from high performance professional graphics solutions to mobile and embedded GPUs
* Native multi-GPU support for high density computing with Quadro CUDA-enabled GPUs
* Direct driver and assembly level access through CUDA for research and language development
CUDA technology
GPU computing with CUDA technology is an innovative combination of computing features in next generation NVIDIA GPUs that are accessed through the standard ‘C’ language. Where previous generation GPUs were based on “streaming shader programs”, CUDA programmers use ‘C’ to create programs called kernels that use many threads to operate on large quantities of data in parallel. In contrast to multi-core CPUs, where only a few threads execute at the same time, NVIDIA GPUs featuring CUDA technology process thousands of threads simultaneously enabling high computational throughput across large amounts of data.
GPGPU, or "General-Purpose Computation on GPUs", has traditionally required the use of a graphics API such as OpenGL, which presents the wrong abstraction for general-purpose parallel computation. Therefore, traditional GPGPU applications are difficult to write, debug, and optimize. NVIDIA GPU Computing with CUDA enables direct implementation of parallel computations in the C language using an API designed for general-purpose computation.
One of the most important innovations offered by CUDA technology is the ability for threads on NVIDIA GPUs to cooperate when solving a problem. By enabling threads to communicate, CUDA technology allows applications to operate more efficiently. NVIDIA GPUs featuring CUDA technology have an on-chip Parallel Data Cache that developers can use to store frequently used information directly on the GPU. Storing information on the GPU allows computing threads to instantly share information rather than wait for data from much slower, off-chip DRAMs. This advance in technology enables users to find the answers to complex computational problems much more quickly than using traditional architectures or GPGPU that is limited to graphics API-based GPU programming.
Why Use CUDA technology?
Performance. NVIDIA GPUs offer incredible performance for data-intensive applications. CUDA technology provides a standard, widely available solution for delivering new applications with unprecedented capability.
Compatibility. Applications developed with the CUDA C-compiler are compatible with future generation GPUs from NVIDIA. Developers investing in GPU computing will immediately benefit from the performance of current GPUs and be confident in NVIDIA’s future investment in high performance technology for GPU computing.
Productivity. Developers wanting to tap into NVIDIA GPU computing power can now use the industry standard “C” language for software development. CUDA provides a complete development solution that integrates CPU and GPU software to enable developers to quickly provide new features and greater value for their customers.
Scalability. Applications developed with CUDA technology scale in performance and features across the full line of NVIDIA G8X and future GPUs from embedded form factors to high-performance professional graphics solutions using multiple GPUs. The power of CUDA performance is now available in virtually any system class, from cluster computing installations to consumer products.
NVIDIA CUDA
Revolutionary GPU Computing
NVIDIA® CUDA™ technology is a fundamentally new computing architecture that enables the GPU to solve complex computational problems in consumer, business, and technical applications. CUDA (Compute Unified Device Architecture) technology gives computationally intensive applications access to the tremendous processing power of NVIDIA graphics processing units (GPUs) through a revolutionary new programming interface. Providing orders of magnitude more performance and simplifying software development by using 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.
Developing with CUDA
The CUDA Toolkit is a complete software development solution for programming CUDA-enabled GPUs. The Toolkit includes standard FFT and BLAS libraries, a C-compiler for the NVIDIA GPU and a runtime driver. The CUDA runtime driver is a separate standalone driver that interoperates with OpenGL and Microsoft® DirectX® drivers from NVIDIA. CUDA technology is currently supported on the Linux and Microsoft® Windows® XP operating systems.
The CUDA Developer SDK provides examples with source code to help you get started with CUDA. Examples include:
* Parallel bitonic sort
* Matrix multiplication
* Matrix transpose
* Performance profiling using timers
* Parallel prefix sum (scan) of large arrays
* Image convolution
* 1D DWT using Haar wavelet
* OpenGL and Direct3D graphics interoperation examples
* CUDA BLAS and FFT library usage examples
* CPU-GPU C- and C++-code integration
Technology Features
* Unified hardware and software solution for parallel computing on CUDA-enabled NVIDIA GPUs
* CUDA-enabled GPUs support the Parallel Data Cache and Thread Execution Manager for high performance computing
* Standard C programming language enabled on a GPU
* Standard numerical libraries for FFT (Fast Fourier Transform) and BLAS (Basic Linear Algebra Subroutines)
* Dedicated CUDA driver for computing
* Optimized upload and download path from the CPU to CUDA-enabled GPU
* CUDA driver interoperates with OpenGL and DirectX graphics drivers
* Support for Linux and Windows XP operating systems
* Scales from high performance professional graphics solutions to mobile and embedded GPUs
* Native multi-GPU support for high density computing with Quadro CUDA-enabled GPUs
* Direct driver and assembly level access through CUDA for research and language development
CUDA technology
GPU computing with CUDA technology is an innovative combination of computing features in next generation NVIDIA GPUs that are accessed through the standard ‘C’ language. Where previous generation GPUs were based on “streaming shader programs”, CUDA programmers use ‘C’ to create programs called kernels that use many threads to operate on large quantities of data in parallel. In contrast to multi-core CPUs, where only a few threads execute at the same time, NVIDIA GPUs featuring CUDA technology process thousands of threads simultaneously enabling high computational throughput across large amounts of data.
GPGPU, or "General-Purpose Computation on GPUs", has traditionally required the use of a graphics API such as OpenGL, which presents the wrong abstraction for general-purpose parallel computation. Therefore, traditional GPGPU applications are difficult to write, debug, and optimize. NVIDIA GPU Computing with CUDA enables direct implementation of parallel computations in the C language using an API designed for general-purpose computation.
One of the most important innovations offered by CUDA technology is the ability for threads on NVIDIA GPUs to cooperate when solving a problem. By enabling threads to communicate, CUDA technology allows applications to operate more efficiently. NVIDIA GPUs featuring CUDA technology have an on-chip Parallel Data Cache that developers can use to store frequently used information directly on the GPU. Storing information on the GPU allows computing threads to instantly share information rather than wait for data from much slower, off-chip DRAMs. This advance in technology enables users to find the answers to complex computational problems much more quickly than using traditional architectures or GPGPU that is limited to graphics API-based GPU programming.
Why Use CUDA technology?
Performance. NVIDIA GPUs offer incredible performance for data-intensive applications. CUDA technology provides a standard, widely available solution for delivering new applications with unprecedented capability.
Compatibility. Applications developed with the CUDA C-compiler are compatible with future generation GPUs from NVIDIA. Developers investing in GPU computing will immediately benefit from the performance of current GPUs and be confident in NVIDIA’s future investment in high performance technology for GPU computing.
Productivity. Developers wanting to tap into NVIDIA GPU computing power can now use the industry standard “C” language for software development. CUDA provides a complete development solution that integrates CPU and GPU software to enable developers to quickly provide new features and greater value for their customers.
Scalability. Applications developed with CUDA technology scale in performance and features across the full line of NVIDIA G8X and future GPUs from embedded form factors to high-performance professional graphics solutions using multiple GPUs. The power of CUDA performance is now available in virtually any system class, from cluster computing installations to consumer products.
