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A graphics processing unit (GPU) is a specialized electronic circuit initially designed to accelerate computer graphics and image processing. After their initial design, GPUs were found to be useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. Other non-graphical uses include the training of neural networks and cryptocurrency mining.
A fat binary is a computer executable program or library which has been expanded with code native to multiple instruction sets which can consequently be run on multiple processor types. This results in a file larger than a normal one-architecture binary file, thus the name.
LLVM is a set of compiler and toolchain technologies that can be used to develop a frontend for any programming language and a backend for any instruction set architecture. LLVM is designed around a language-independent intermediate representation (IR) that serves as a portable, high-level assembly language that can be optimized with a variety of transformations over multiple passes. The name LLVM originally stood for Low Level Virtual Machine, though the project has expanded and the name is no longer officially an initialism.
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.
Mesa, also called Mesa3D and The Mesa 3D Graphics Library, is an open source implementation of OpenGL, Vulkan, and other graphics API specifications. Mesa translates these specifications to vendor-specific graphics hardware drivers.
A free and open-source graphics device driver is a software stack which controls computer-graphics hardware and supports graphics-rendering application programming interfaces (APIs) and is released under a free and open-source software license. Graphics device drivers are written for specific hardware to work within a specific operating system kernel and to support a range of APIs used by applications to access the graphics hardware. They may also control output to the display if the display driver is part of the graphics hardware. Most free and open-source graphics device drivers are developed by the Mesa project. The driver is made up of a compiler, a rendering API, and software which manages access to the graphics hardware.
Compute Unified Device Architecture (CUDA) is a 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 (GPGPU). CUDA API and its runtime: The CUDA API is an extension of the C programming language that adds the ability to specify thread-level parallelism in C and also to specify GPU device specific operations (like moving data between the CPU and the GPU). 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.
PGI was a company that produced a set of commercially available Fortran, C and C++ compilers for high-performance computing systems. On July 29, 2013, Nvidia acquired The Portland Group, Inc. As of August 5, 2020, the "PGI Compilers and Tools" technology is a part of the Nvidia HPC SDK product available as a free download from Nvidia.
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.
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.
OpenACC is a programming standard for parallel computing developed by Cray, CAPS, Nvidia and PGI. The standard is designed to simplify parallel programming of heterogeneous CPU/GPU systems.
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).
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.
Vulkan is a low-level low-overhead, cross-platform API and open standard for 3D graphics and computing. It was intended to address the shortcomings of OpenGL, and allow developers more control over the GPU. It is designed to support a wide variety of GPUs, CPUs and operating systems, it is also designed to work with modern multi-core CPUs.
Multidimensional Digital Signal Processing (MDSP) refers to the extension of Digital signal processing (DSP) techniques to signals that vary in more than one dimension. While conventional DSP typically deals with one-dimensional data, such as time-varying audio signals, MDSP involves processing signals in two or more dimensions. Many of the principles from one-dimensional DSP, such as Fourier transforms and filter design, have analogous counterparts in multidimensional signal processing.
GPUOpen is a middleware software suite originally developed by AMD's Radeon Technologies Group that offers advanced visual effects for computer games. It was released in 2016. GPUOpen serves as an alternative to, and a direct competitor of Nvidia GameWorks. GPUOpen is similar to GameWorks in that it encompasses several different graphics technologies as its main components that were previously independent and separate from one another. However, GPUOpen is partially open source software, unlike GameWorks which is proprietary and closed.
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), and OpenCL.
oneAPI is an open standard, adopted by Intel, for a unified application programming interface (API) intended to be used across different computing accelerator (coprocessor) architectures, including GPUs, AI accelerators and field-programmable gate arrays. It is intended to eliminate the need for developers to maintain separate code bases, multiple programming languages, tools, and workflows for each architecture.
References
- ↑ "CUDA LLVM Compiler". NVIDIA Developer. Retrieved Apr 6, 2016.
- ↑ "CUDA C++ Programming Guide". NVIDIA Documentation Hub. Retrieved 2019-06-28.
General
- David B. Kirk, and Wen-mei W. Hwu. Programming massively parallel processors: a hands-on approach. Morgan Kaufmann, 2010.
- "NVIDIA CUDA Compiler Driver NVCC". NVIDIA Documentation Hub. Archived from the original on Oct 13, 2023.
- "CUDPP". GPGPU. Archived from the original on Nov 17, 2018.