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Data parallelism: History, Applications, Measurement & Products

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…

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Data parallelism topic overview

The analysis highlights History, Applications, Measurement and Products as prominent areas in the source structure around Data parallelism.

Related topics
32
Source areas
7
Connected nodes
39
Extracted relationships
24
Concept neighborhoods
17
Bridge connections
39

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.

History · 9 topics
Applications · 6 topics
Data parallel programming environments · 6 topics
Example · 5 topics
Overview · 3 topics
Description · 2 topics
Mixed data and task parallelism · 1 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

History

Description

Example

Mixed data and task parallelism

Data parallel programming environments

Applications

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 Data parallelism connects Entity context

The extracted context around Data parallelism shows recurring relationship patterns in the source. For example, Data parallelism → Concurrency, Connection Machines, Data Parallel Haskell, Exploitation, Futhark, GPUs, In, Most, NESL, Solomon, The, The Solomon, These, This, Today Another extracted example is Data parallelism → Data, Driving, Sciences. Use these groups to spot repeated connection types before inspecting the individual relationships.

Data parallelism

Top relations

related to history · 15
Data parallelism → Concurrency, Connection Machines, Data Parallel Haskell, Exploitation, Futhark, GPUs, In, Most, NESL, Solomon, The, The Solomon, These, This, Today
has application · 3
Data parallelism → Data, Driving, Sciences
related to Description · 3
Data parallelism → For, In, SIMD

Important terminology

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

Important terminology

data parallel parallelism task time processors programming units matrix array computing processing addition sequential execution elements mixed applications model multiplication

Data parallelism relationships Subject–Predicate–Object triples

TTTA extracted 24 structured relationships around Data parallelism. Examples in this analysis include Data Parallel Haskell → instance of → This work was continued by other languages and Data parallelism → has application → Data. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Data Parallel Haskellinstance ofThis work was continued by other languages0.80text
Futharkinstance ofThis work was continued by other languages0.80text
although arbitrary nested data parallelism is not widely available in current data-parallel programming languagesinstance ofThis work was continued by other languages0.80text
Data parallelismhas applicationData0.60section
Data parallelismhas applicationSciences0.60section
Data parallelismhas applicationDriving0.60section
Data parallelismrelated to DescriptionIn0.60section
Data parallelismrelated to DescriptionSIMD0.60section
Data parallelismrelated to DescriptionFor0.60section
Data parallelismrelated to historyExploitation0.60section
Data parallelismrelated to historySolomon0.60section
Data parallelismrelated to historyThe Solomon0.60section

Related concept clusters Concept neighborhoods

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

  • Data parallelism
    • Parallelism
    • Parallel
    • Task
    • Programming
    • Applications
    • Processing
    • Computing
    • Mixed
    • Time
    • Environments
    • Large
    • Model
  • data parallelism
    • Parallelism
    • Task
    • Parallel
    • Mixed
    • Programming
    • Applications
    • Processing
    • Model
    • Computing
    • Time
    • Environments
    • Large
  • parallel computing
    • Applications
    • Programming
    • Environments
    • Large
    • Model
    • Parallel
    • Parallelism
    • Processing
    • Processors
    • Data
    • Task
    • Job
  • task parallelism
    • Parallelism
    • Task
    • Mixed
    • Data
    • Applications
    • Parallel
    • Programming
    • Model
    • Environments
    • Processing
    • Different
    • Across
  • locality of data references
    • Parallelism
    • Parallel
    • Task
    • Programming
    • Applications
    • Processing
    • Computing
    • Mixed
    • Time
    • Environments
    • Large
    • Model
  • data parallel haskell
    • Parallelism
    • Parallel
    • Programming
    • Task
    • Model
    • Processors
    • Applications
    • Processing
    • Computing
    • Mixed
    • Job
    • Time
  • data parallel
    • Parallelism
    • Parallel
    • Programming
    • Task
    • Model
    • Processors
    • Applications
    • Processing
    • Computing
    • Mixed
    • Job
    • Time
  • big data
    • Parallelism
    • Parallel
    • Task
    • Programming
    • Applications
    • Processing
    • Computing
    • Mixed
    • Time
    • Environments
    • Large
    • Model

Connections between topic areas Semantic bridges

For Data parallelism, one of the stronger structural bridges in this analysis connects Data parallelism with History. 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
Data parallelism — History · splits 30 ⟂ 10
Data parallelism — Data parallel programming environments · splits 33 ⟂ 7
Data parallelism — Applications · splits 33 ⟂ 7
Data parallelism — Example · splits 34 ⟂ 6
Data parallelism — Overview · splits 36 ⟂ 4
Data parallelism — Description · splits 37 ⟂ 3

Map overview Semantic statistics

Data parallelism

Nodes40
Edges39
Triples24
Avg. degree1.95
Density0.05
Components1

Source & methodology

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

Source: Wikipedia — Data parallelism · EN edition · Analysis: TopicsToTalkAbout

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