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In computing, single program, multiple data (SPMD) is a term that has been used to refer to computational models for exploiting parallelism whereby multiple processors cooperate in the execution of a program in order to obtain results faster.
The analysis highlights History and Products as prominent areas in the source structure around Single program, multiple data.
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.
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.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See recurring relationship patterns around Single program, multiple data before inspecting the individual extracted relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
spmd parallel program simd memory data multiple processors different execution shared processes distributed programming ibm single model used computational processor
TTTA extracted 6 structured relationships around Single program, multiple data. Examples in this analysis include active messages → instance of → It is also a prerequisite for research concepts and Barrier synchronization may also be implemented by messages → instance of → Other parallelization directives. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| active messages | instance of | It is also a prerequisite for research concepts | 0.80 | text |
| distributed shared memory..mw-parser-output .hlist dl | instance of | It is also a prerequisite for research concepts | 0.80 | text |
| .mw-parser-output .hlist ol | instance of | It is also a prerequisite for research concepts | 0.80 | text |
| .mw-parser-output .hlist ul | instance of | It is also a prerequisite for research concepts | 0.80 | text |
| Barrier synchronization may also be implemented by messages | instance of | Other parallelization directives | 0.80 | text |
| InfiniBand or Omni-Path | instance of | or specialized high-speed interconnects | 0.80 | text |
The concept neighborhoods around Single program, multiple data bring nearby vocabulary together. In this analysis, examples include Data, Execution and Single. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Single program, multiple data, one of the stronger structural bridges in this analysis connects Single program, multiple data with Operation. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Single program, multiple data to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Single program, multiple data · EN edition · Analysis: TopicsToTalkAbout