Data parallelism

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Map view:

Zoom: 100%

More settings

Connection length 100% Label size 100% Node size 100% Line width 100% Label width 100%

Export map as:

Data parallelism topic overview

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

Related topics: 31
Source areas: 7
Connected nodes: 38
Extracted relationships: 17
Related term clusters: 17
Bridge connections: 38

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 · 8 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.

Start with your topic. Discover where to go next.

Explore different angles and find fresh ideas to shape your next piece of content.

Data parallelism

7Data-intensive computing · Parallel computing · Data parallel

24Task parallelism · Locality of reference · Vector processor

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

Circuit simulation

Data parallel programming environments

Applications

For the semantics nerds

You can skip this section if you’re here for content ideas and keyword inspiration.

Advanced semantic analysis

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, NESL, Solomon, The Solomon, Today Another extracted example is Data parallelism → Data, Driving, Sciences. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

related to history · 10

Data parallelism → Concurrency, Connection Machines, Data Parallel Haskell, Exploitation, Futhark, GPUs, NESL, Solomon, The Solomon, Today

has application · 3

Data parallelism → Data, Driving, Sciences

related to Description · 1

Data parallelism → 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 17 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.

Subject	Predicate	Object	Confidence	Src
Data Parallel Haskell	instance of	This work was continued by other languages	0.80	text
Futhark	instance of	This work was continued by other languages	0.80	text
although arbitrary nested data parallelism is not widely available in current data-parallel programming languages	instance of	This work was continued by other languages	0.80	text
Data parallelism	has application	Data	0.60	section
Data parallelism	has application	Sciences	0.60	section
Data parallelism	has application	Driving	0.60	section
Data parallelism	related to Description	SIMD	0.60	section
Data parallelism	related to history	Exploitation	0.60	section
Data parallelism	related to history	Solomon	0.60	section
Data parallelism	related to history	The Solomon	0.60	section
Data parallelism	related to history	Concurrency	0.60	section
Data parallelism	related to history	Connection Machines	0.60	section

Related concept clusters Related term clusters

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 ⟂ 9

Data parallelism — Data parallel programming environments · splits 32 ⟂ 7

Data parallelism — Applications · splits 32 ⟂ 7

Data parallelism — Example · splits 33 ⟂ 6

Data parallelism — Overview · splits 35 ⟂ 4

Data parallelism — Description · splits 36 ⟂ 3

Map overview Semantic statistics

Data parallelism

Nodes39

Edges38

Triples17

Avg. degree1.95

Density0.051282

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

Data parallelism: History, Applications, Measurement & Products

Data parallelism topic overview

What this topic covers Research coverage

Key facts & relationships

Start with your topic. Discover where to go next.

Questions to explore

Explore all related topics Closing gaps

Overview

History

Description

Example

Mixed data and task parallelism

Data parallel programming environments

Applications

For the semantics nerds

Advanced semantic analysis

How Data parallelism connects Entity context

Data parallelism

Top relations

Important terminology

Important terminology

Data parallelism relationships Subject–Predicate–Object triples

Related concept clusters Related term clusters

Connections between topic areas Semantic bridges

Map overview Semantic statistics

Data parallelism

Source & methodology