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GPGPU
General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. Essentially, a GPGPU pipeline is a kind of parallel processing between one or more GPUs and CPUs that analyzes data as if it were in image or other graphic form. While GPUs operate at lower frequencies, they typically have many times the number of cores. Thus, GPUs can process far more pictures and graphical data per second than a traditional CPU. Migrating data into graphical form and then using the GPU to scan and analyze it can create a large speedup. GPGPU pipelines were developed at the beginning of the 21st century for grap ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Graphics Processing Unit
A graphics processing unit (GPU) is a specialized electronic circuit designed for digital image processing and to accelerate computer graphics, being present either as a discrete video card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles. GPUs were later found to be useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. The ability of GPUs to rapidly perform vast numbers of calculations has led to their adoption in diverse fields including artificial intelligence (AI) where they excel at handling data-intensive and computationally demanding tasks. Other non-graphical uses include the training of neural networks and cryptocurrency mining. History 1970s Arcade system boards have used specialized graphics circuits since the 1970s. In early video game hardware, RAM for frame buffers was expensive, so video chips composited data together as the display was being scann ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
OpenCL
OpenCL (Open Computing Language) is a software framework, framework for writing programs that execute across heterogeneous computing, heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators. OpenCL specifies a programming language (based on C99) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the OpenCL compute devices, compute devices. OpenCL provides a standard interface for parallel computing using Task parallelism, task- and Data parallelism, data-based parallelism. OpenCL is an open standard maintained by the Khronos Group, a Non-profit organization, non-profit, open standards organisation. Conformant implementations (passed the Conformance Test Suite) are available from a range of companies including AMD, Arm (company), Arm, Caden ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BrookGPU
In computing, the Brook programming language and its implementation BrookGPU were early and influential attempts to enable general-purpose computing on graphics processing units (GPGPU). Brook, developed at Stanford University graphics group, was a compiler and runtime implementation of a stream programming language targeting modern, highly parallel GPUs such as those found on ATI or Nvidia graphics cards. BrookGPU compiled programs written using the Brook stream programming language, which is a variant of ANSI C. It could target OpenGL v1.3+, DirectX v9+ or AMD's Close to Metal for the computational backend and ran on both Microsoft Windows and Linux. For debugging, BrookGPU could also simulate a virtual graphics card on the CPU. Status The last major beta release (v0.4) was in October 2004 but renewed development began and stopped again in November 2007 with a v0.5 beta 1 release. The new features of v0.5 include a much upgraded and faster OpenGL backend which uses frameb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nvidia Tesla
Nvidia Tesla is the former name for a line of products developed by Nvidia targeted at stream processing or GPGPU, general-purpose graphics processing units (GPGPU), named after pioneering electrical engineer Nikola Tesla. Its products began using GPUs from the GeForce 8 series, G80 series, and have continued to accompany the release of new chips. They are programmable using the CUDA or OpenCL application programming interface, APIs. The Nvidia Tesla product line competed with AMD's Radeon Instinct and Intel Xeon Phi lines of deep learning and GPU cards. Nvidia retired the Tesla brand in May 2020, reportedly because of potential confusion with the Tesla, Inc., brand of cars. Its new GPUs are branded Nvidia Data Center GPUs as in the Ampere (microarchitecture), Ampere-based A100 GPU. Nvidia DGX servers feature Nvidia GPGPUs. Overview Offering computational power much greater than traditional Central processing units, microprocessors, the Tesla products targeted the high-perfo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parallel Computing
Parallel computing is a type of computing, computation in which many calculations or Process (computing), processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: Bit-level parallelism, bit-level, Instruction-level parallelism, instruction-level, Data parallelism, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.S.V. Adve ''et al.'' (November 2008)"Parallel Computing Research at Illinois: The UPCRC Agenda" (PDF). Parallel@Illinois, University of Illinois at Urbana-Champaign. "The main techniques for these performance benefits—increased clock frequency and smarter but increasingly complex architectures—are now hitting the so-called power wall. The computer industry has accepted that future performance inc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AMD Instinct
AMD Instinct is AMD's brand of data center Graphics processing unit, GPUs. It replaced AMD's AMD FirePro, FirePro S brand in 2016. Compared to the Radeon brand of mainstream consumer/gamer products, the Instinct product line is intended to accelerate deep learning, artificial neural network, and high-performance computing/general-purpose computing on graphics processing units, GPGPU applications. The AMD Instinct product line directly competes with Nvidia's Nvidia Tesla, Tesla and Intel's Xeon Phi and Intel Data Center GPU, Data Center GPU lines of machine learning and GPGPU cards. The brand was originally known as AMD Radeon Instinct, but AMD dropped the Radeon brand from the name before AMD Instinct MI100 was introduced in November 2020. In June 2022, supercomputers based on AMD's Epyc CPUs and Instinct GPUs took the lead on the Green500 list of the most power-efficient supercomputers with over 50% lead over any other, and held the top first 4 spots. One of them, the AMD-bas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CUDA
In computing, CUDA (Compute Unified Device Architecture) is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs. CUDA was created by Nvidia in 2006. When it was first introduced, the name was an acronym for ''Compute Unified Device Architecture'', but Nvidia later dropped the common use of the acronym and now rarely expands it. CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications. CUDA is designed to work with programming languages such as C, C++, Fortran, Python and Julia. This accessibility makes ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lib Sh
Sh was an early metaprogramming language for programmable GPUs. It offered a general-purpose programming language, following a stream-processing model. Programs written in Sh could either run on CPUs or GPUs, obviating the need to write programs in a mix of two programming languages as was the case with earlier GPU programming systems such as Cg or HLSL. As of August 2006, it is no longer maintained. RapidMind Inc. was formed to commercialize the research behind Sh. RapidMind was then bought by Intel and ceased Sh development as well. See also *BrookGPU *CUDA * Close to Metal *OpenCL OpenCL (Open Computing Language) is a software framework, framework for writing programs that execute across heterogeneous computing, heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), di ... * RapidMind References External links * GPGPU GPGPU libraries {{Compu-graphics-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stream Processing
In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views Stream (computing), streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and Distributed computing, distributed data processing. Stream processing systems aim to expose parallel computing, parallel processing for data streams and rely on streaming algorithms for efficient implementation. The Solution stack, software stack for these systems includes components such as programming models and query languages, for expressing computation; data stream management system, stream management systems, for distribution and scheduling (computing), scheduling; and hardware components for hardware acceleration, acceleration including floating-point units, graphics processing units, and field-programmable gate a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Shader
In computer graphics, a shader is a computer program that calculates the appropriate levels of light, darkness, and color during the rendering of a 3D scene—a process known as '' shading''. Shaders have evolved to perform a variety of specialized functions in computer graphics special effects and video post-processing, as well as general-purpose computing on graphics processing units. Traditional shaders calculate rendering effects on graphics hardware with a high degree of flexibility. Most shaders are coded for (and run on) a graphics processing unit (GPU), though this is not a strict requirement. ''Shading languages'' are used to program the GPU's rendering pipeline, which has mostly superseded the fixed-function pipeline of the past that only allowed for common geometry transforming and pixel-shading functions; with shaders, customized effects can be used. The position and color ( hue, saturation, brightness, and contrast) of all pixels, vertices, and/or ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nvidia DGX
The Nvidia DGX (Deep GPU Xceleration) represents a series of servers and workstations designed by Nvidia, primarily geared towards enhancing deep learning applications through the use of general-purpose computing on graphics processing units (GPGPU). These systems typically come in a rackmount format featuring high-performance x86 server CPUs on the motherboard. The core feature of a DGX system is its inclusion of 4 to 8 Nvidia Tesla GPU modules, which are housed on an independent system board. These GPUs can be connected either via a version of the SXM socket or a PCIe x16 slot, facilitating flexible integration within the system architecture. To manage the substantial thermal output, DGX units are equipped with heatsinks and fans designed to maintain optimal operating temperatures. This framework makes DGX units suitable for computational tasks associated with artificial intelligence and machine learning models. Models Pascal - Volta DGX-1 DGX-1 servers feature 8 GPU ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DirectCompute
Microsoft DirectCompute is an application programming interface (API) that supports running compute kernels on general-purpose computing on graphics processing units on Microsoft's Windows Vista, Windows 7 and later versions. DirectCompute is part of the Microsoft DirectX collection of APIs, and was initially released with the DirectX 11 API but runs on graphics processing units that use either DirectX 10 or DirectX 11. The DirectCompute architecture shares a range of computational interfaces with its competitors: OpenCL from Khronos Group, compute shaders in OpenGL, and CUDA from NVIDIA. The DirectCompute API brings enhanced multi-threading capabilities to leverage the emerging advanced compute resources. The API is designed for non-graphical applications to access and use GPU resources. Compute Pipeline The Compute pipeline is a type of graphics pipeline used for dispatching and executing compute shaders. Compute pipelines are run through compute command lists, which are r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
