of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but...

computations in parallel. GPUs are massively parallel architecture with tens of thousands of threads. One approach is grid computing, where the processing...

In parallel computing, granularity (or grain size) of a task is a measure of the amount of work (or computation) which is performed by that task. Another...

In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel...

Explicitly parallel instruction computing (EPIC) is a term coined in 1997 by the HP–Intel alliance to describe a computing paradigm that researchers had...

and scheduled by software. The newest manifestation of cluster computing is cloud computing. The components of a cluster are usually connected to each other...

common goal for their work. The terms "concurrent computing", "parallel computing", and "distributed computing" have much overlap, and no clear distinction...

of the analytical engine's computing unit (the mill) in 1888. He gave a successful demonstration of its use in computing tables in 1906. In his work...

High-performance computing High-performance technical computing Jungle computing Metacomputing Nvidia Tesla Personal Supercomputer Parallel computing Supercomputing...

Complexity Cybernetics Dennett's three stances Granular computing Granularity (parallel computing) High- and low-level Levels of analysis Meta-system Multiple...

Concurrent computing is a form of computing in which several computations are executed concurrently—during overlapping time periods—instead of sequentially—with...

Gelernter is known for contributions to parallel computation in the 1980s, and for books on topics such as computed worlds (Mirror Worlds). Gelernter is...

Optical computing or photonic computing uses light waves produced by lasers or incoherent sources for data processing, data storage or data communication...

EPCC, formerly the Edinburgh Parallel Computing Centre, is a supercomputing centre based at the University of Edinburgh. Since its foundation in 1990,...

and create high performance computing systems. Recently[when?], HPC systems have shifted from supercomputing to computing clusters and grids. Because...

In computing, load balancing is the process of distributing a set of tasks over a set of resources (computing units), with the aim of making their overall...

introduced the DirectCompute GPU computing API, released with the DirectX 11 API. Alea GPU, created by QuantAlea, introduces native GPU computing capabilities...

Tools for parallel computing. IPython is a NumFOCUS fiscally sponsored project. IPython is based on an architecture that provides parallel and distributed...

Proceedings of International Conference on Parallel Computing Technologies. Novosibirsk. 1991. David M. W. Powers, Parallel Unification: Practical Complexity,...

Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system...

Unified Parallel C (UPC) is an extension of the C programming language designed for high-performance computing on large-scale parallel machines, including...

Commodity computing (also known as commodity cluster computing) involves the use of large numbers of already-available computing components for parallel computing...

Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and...

and grid computing but has, since the mid-1990s, largely been supplanted by the much more successful MPI standard for message passing on parallel machines...

CUDA is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing...

Unconventional computing (also known as alternative computing or nonstandard computation) is computing by any of a wide range of new or unusual methods...

of a sequence of computing processes (commands, program runs, tasks, threads, procedures, etc.), conceptually executed in parallel, with the output stream...

Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes...

A subtype of parallel algorithms, distributed algorithms, are algorithms designed to work in cluster computing and distributed computing environments...

in modern computing, including: Operating systems and embedded systems Distributed systems, parallel computing, and high-performance computing Database...