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Parallel computing: History, Background & Granularity

Parallel computing is a type of computation in which many calculations or 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, instruction-level, data, and task parallelism. Parallelism has long been…

Language: English [EN]
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Parallel computing topic overview

The analysis highlights History, Background and Granularity as prominent areas in the source structure around Parallel computing. 1 topic appears in more than one source area, which can help identify connections that are less obvious in a linear reading.

Related topics
287
Source areas
9
Connected nodes
297
Extracted relationships
88
Concept neighborhoods
56
Bridge connections
297

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Overview · 102 topics
Background · 42 topics
Algorithmic methods · 37 topics
Hardware · 27 topics
Software · 24 topics
Granularity · 21 topics
History · 20 topics
Biological brain as massively parallel computer · 10 topics
Fault tolerance · 5 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Background

Granularity

Hardware

Software

Algorithmic methods

Fault tolerance

History

Biological brain as massively parallel computer

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Parallel computing connects Entity context

The extracted context around Parallel computing shows recurring relationship patterns in the source. For example, Parallel computing → As, Barnes, Bayesian, Common, Constraint, Cooley, CSPs, Data, Dense, Dynamic, Fields, Fourier, Graph, HBJ, Hut, Lattice Boltzmann, Markov, Monte Carlo, N-body, Particle Another extracted example is Parallel computing → Asynchronous, August, Baran, BIMNICS, Bio-Inspired Models, Boolean Satisfiability, Computing Systems, GIM International, Information, Network, Photogrammetry, Rodriguez, S2CID, Sechin, Villagra. Use these groups to spot repeated connection types before inspecting the individual relationships.

Parallel computing

Top relations

has method · 23
Parallel computing → As, Barnes, Bayesian, Common, Constraint, Cooley, CSPs, Data, Dense, Dynamic, Fields, Fourier, Graph, HBJ, Hut, Lattice Boltzmann, Markov, Monte Carlo, N-body, Particle
related to Further reading · 15
Parallel computing → Asynchronous, August, Baran, BIMNICS, Bio-Inspired Models, Boolean Satisfiability, Computing Systems, GIM International, Information, Network, Photogrammetry, Rodriguez, S2CID, Sechin, Villagra
related to Automatic parallelization · 11
Parallel computing → Automatic, Despite, FPGAs, Mainstream, Mitrion-C, Parallel Haskell, SequenceL, SISAL, SystemC, Verilog, VHDL
related to background · 8
Parallel computing → Historically, Only, Parallel, The, These, This, To, Traditionally
related to External links · 6
Parallel computing → Archive, Building Parallel Programs, Ian FosterInternet Parallel Computing, Introduction, Lawrence Livermore National Laboratory, Parallel ComputingDesigning
related to Fault tolerance · 4
Parallel computing → Although, Parallel, These, This
related to Disadvantages · 3
Parallel computing → Parallel, Specifically, Therefore
is a · 1
Parallel computing → type of computation in which many calculations or processes are carried out simultaneously

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

parallel computer computing parallelism processors program memory processor processing one multiple computers use distributed data threads programming instructions known system

Parallel computing relationships Subject–Predicate–Object triples

TTTA extracted 88 structured relationships around Parallel computing. Examples in this analysis include Parallel computing → is a → type of computation in which many calculations or processes are carried out simultaneously and a single computer with multiple processors → instance of → The processing elements can be diverse and include resources. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Parallel computingis atype of computation in which many calculations or processes are carried out simultaneously0.90text
a single computer with multiple processorsinstance ofThe processing elements can be diverse and include resources0.80text
several networked computersinstance ofThe processing elements can be diverse and include resources0.80text
specialized hardwareinstance ofThe processing elements can be diverse and include resources0.80text
or any combination of the aboveinstance ofThe processing elements can be diverse and include resources0.80text
data persistenceinstance ofprimarily focusing on computational aspect and ignoring extrinsic factors0.80text
I/O operationsinstance ofprimarily focusing on computational aspect and ignoring extrinsic factors0.80text
and memory access overheads.Gustafson's lawinstance ofprimarily focusing on computational aspect and ignoring extrinsic factors0.80text
Universal Scalability Law give a more realistic assessment of the parallel performance.DependenciesUnderstanding data dependencies is fundamental in implementing parallel algorithmsinstance ofprimarily focusing on computational aspect and ignoring extrinsic factors0.80text
Universal Scalability Law give a more realistic assessment of the parallel performanceinstance ofprimarily focusing on computational aspect and ignoring extrinsic factors0.80text
PGASinstance ofdistributed shared memory space can be implemented using the programming model0.80text
Cerberusinstance ofutilized by protocols0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Parallel computing bring nearby vocabulary together. In this analysis, examples include Parallel, Computer and Computers. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Parallel computing
    • Parallel
    • Computer
    • Computers
    • Programming
    • Distributed
    • Program
    • Processors
    • Parallelism
    • Programs
    • Processing
    • Task
    • Multi-core
  • parallel computing
    • Parallel
    • Computer
    • Computers
    • Distributed
    • Programming
    • Program
    • Multi-core
    • Systems
    • Processors
    • Parallelism
    • Programs
    • Processing
  • data
    • Different
    • Program
    • Task
    • Processing
    • Parallel
    • Also
    • Single
    • Access
    • Memory
    • Instruction
    • Instructions
    • Programming
  • task parallelism
    • Task
    • Parallelization
    • Use
    • Hardware
    • Computer
    • Programming
    • Computers
    • Processing
    • Architecture
    • Processor
    • First
    • Also
  • high-performance computing
    • Parallel
    • Distributed
    • Computer
    • Multi-core
    • Systems
    • Use
    • Processors
    • Parallelism
    • Parallelization
    • Also
    • Hardware
    • Used
  • computer architecture
    • Systems
    • Parallel
    • Processing
    • Architecture
    • Computer
    • Memory
    • Processor
    • Processors
    • Multi-core
    • Computing
    • System
    • Distributed
  • multiple cpu cores
    • Single
    • Processing
    • Instructions
    • Execution
    • Processor
    • Also
    • One
    • Threads
    • Processors
    • Program
    • Task
    • Hardware
  • computer science
    • Systems
    • Parallel
    • Processing
    • Architecture
    • Memory
    • Processor
    • Processors
    • Computing
    • System
    • Distributed
    • Parallelism
    • Also

Connections between topic areas Semantic bridges

For Parallel computing, one of the stronger structural bridges in this analysis connects Parallel computing with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Parallel computing — Overview · splits 195 ⟂ 103
Parallel computing — Background · splits 255 ⟂ 43
Parallel computing — Algorithmic methods · splits 260 ⟂ 38
Parallel computing — Hardware · splits 270 ⟂ 28
Parallel computing — Software · splits 273 ⟂ 25
Parallel computing — Granularity · splits 276 ⟂ 22
Parallel computing — History · splits 277 ⟂ 21
Parallel computing — Biological brain as massively parallel computer · splits 287 ⟂ 11
Parallel computing — Fault tolerance · splits 292 ⟂ 6

Map overview Semantic statistics

Parallel computing

Nodes298
Edges297
Triples88
Avg. degree1.99
Density0.006711
Components1

Source & methodology

TTTA analyzes the structure around Parallel computing to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Background & Granularity, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Parallel computing · EN edition · Analysis: TopicsToTalkAbout

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