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A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles.
General-purpose computing on graphics processing units 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.
A shading language is a graphics programming language adapted to programming shader effects. Such language forms usually consist of special data types, like "vector", "matrix", "color" and "normal". Due to the variety of target markets for 3D computer graphics, different shading languages have been developed.
In computer science, stream processing is a programming paradigm which views data 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 data processing. Stream processing systems aim to expose parallel processing for data streams and rely on streaming algorithms for efficient implementation. The software stack for these systems includes components such as programming models and query languages, for expressing computation; stream management systems, for distribution and scheduling; and hardware components for acceleration including floating-point units, graphics processing units, and field-programmable gate arrays.
The Brook programming language and its implementation BrookGPU were early and influential attempts to enable general-purpose computing on graphics processing units. 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.
CUDA is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for general purpose processing, an approach called general-purpose computing on GPUs (GPGPU). 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.
Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism.
AMD FireStream was AMD's brand name for their Radeon-based product line targeting stream processing and/or GPGPU in supercomputers. Originally developed by ATI Technologies around the Radeon X1900 XTX in 2006, the product line was previously branded as both ATI FireSTREAM and AMD Stream Processor. The AMD FireStream can also be used as a floating-point co-processor for offloading CPU calculations, which is part of the Torrenza initiative. The FireStream line has been discontinued since 2012, when GPGPU workloads were entirely folded into the AMD FirePro line.
OpenCL is a framework for writing programs that execute across 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 programming languages for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. OpenCL provides a standard interface for parallel computing using task- and data-based parallelism.
Manycore processors are special kinds of multi-core processors designed for a high degree of parallel processing, containing numerous simpler, independent processor cores. Manycore processors are used extensively in embedded computers and high-performance computing.
Parallel Thread Execution is a low-level parallel thread execution virtual machine and instruction set architecture used in Nvidia's CUDA programming environment. The NVCC compiler translates code written in CUDA, a C++-like language, into PTX instructions, and the graphics driver contains a compiler which translates the PTX instructions into the executable binary code which can be run on the processing cores of Nvidia GPUs. The GNU Compiler Collection also has basic ability for PTX generation in the context of OpenMP offloading. Inline PTX assembly can be used in CUDA.
Graphics Core Next (GCN) is the codename for a series of microarchitectures and an instruction set architecture that were developed by AMD for its GPUs as the successor to its TeraScale microarchitecture. The first product featuring GCN was launched on January 9, 2012.
C++ Accelerated Massive Parallelism is a native programming model that contains elements that span the C++ programming language and its runtime library. It provides an easy way to write programs that compile and execute on data-parallel hardware, such as graphics cards (GPUs).
GPULib is discontinued and unsupported software library developed by Tech-X Corporation for accelerating general-purpose scientific computations from within the Interactive Data Language (IDL) using Nvidia's CUDA platform for programming its graphics processing units (GPUs). GPULib provides basic arithmetic, array indexing, special functions, Fast Fourier Transforms (FFT), interpolation, BLAS matrix operations as well as LAPACK routines provided by MAGMA, and some image processing operations. All numeric data types provided by IDL are supported. GPULib is used in medical imaging, optics, astronomy, earth science, remote sensing, and other scientific areas.
Heterogeneous System Architecture (HSA) is a cross-vendor set of specifications that allow for the integration of central processing units and graphics processors on the same bus, with shared memory and tasks. The HSA is being developed by the HSA Foundation, which includes AMD and ARM. The platform's stated aim is to reduce communication latency between CPUs, GPUs and other compute devices, and make these various devices more compatible from a programmer's perspective, relieving the programmer of the task of planning the moving of data between devices' disjoint memories.
Single instruction, multiple threads (SIMT) is an execution model used in parallel computing where single instruction, multiple data (SIMD) is combined with multithreading. It is different from SPMD in that all instructions in all "threads" are executed in lock-step. The SIMT execution model has been implemented on several GPUs and is relevant for general-purpose computing on graphics processing units (GPGPU), e.g. some supercomputers combine CPUs with GPUs.
Standard Portable Intermediate Representation (SPIR) is an intermediate language for parallel compute and graphics by Khronos Group. It is used in multiple execution environments, including the Vulkan graphics API and the OpenCL compute API, to represent a shader or kernel. It is also used as an interchange language for cross compilation.
Digital signal processing (DSP) is a ubiquitous methodology in scientific and engineering computations. In practice, DSP problems are often not only one dimensional. For instance, image data is a 2-D signal and radar is a 3-D signal. While the number of dimensions increases, the time and/or storage complexity of processing digital signals grow dramatically. Therefore, solving multidimensional DSP problems in real-time is extremely difficult.
SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. It is a single-source embedded domain-specific language (eDSL) based on pure C++17. It is a standard developed by Khronos Group, announced in March 2014.
ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing. It offers several programming models: HIP, OpenMP/Message Passing Interface (MPI), OpenCL.
References
- ↑ Introduction to Compute Programming in Metal, 14 October 2014
- ↑ CUDA Tutorial - the Kernel, 11 July 2009