GPGPU

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, significantly broadening their utility in scientific and high-performance computing. CUDA was created by Nvidia starting in 2004 and was officially released in 2007. 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.

OpenCL (Open Computing Language) 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 a programming language (based on C99) 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

use of a graphics processing unit to perform computation in applications traditionally handled by the central processing unit

Nvidia's brand name for their products targeting stream processing and/or general purpose GPU

programming paradigm which gives some degree of parallel processing

codename for a series of microarchitectures and an instruction set

series of video cards

brand name by AMD

brand name by AMD; family of deep learning oriented GPUs

family of GPU microarchitectures by Intel

GPU microarchitecture designed by NVIDIA

a computer cluster that uses GPUs

codename for a family of graphics processing unit microarchitectures

ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains, including general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), and heterogeneous computing. It offers several programming models: HIP (GPU-kernel-based programming), OpenMP (directive-based programming), and OpenCL.

deep learning supercomputer system

execution model used in parallel computing