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In computing, algorithmic skeletons, or parallelism patterns, are a high-level parallel programming model for parallel and distributed computing.
The analysis highlights Works and Products as prominent areas in the source structure around Algorithmic skeleton.
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.
The extracted context around Algorithmic skeleton shows recurring relationship patterns in the source. For example, Algorithmic skeleton → Additionally, As, Both, Calcium, First, Java, Java Generics, Lithium, Muskel, ProActive, Second, Third Another extracted example is Algorithmic skeleton → Among Marrow's, GPU, It, Loop, Marrow, Moreover, OpenCL, PCIe, Pipeline, The. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
skeleton skeletons parallel programming data language code used framework provides performance distributed using type applications patterns mpi programmers library functional
TTTA extracted 84 structured relationships around Algorithmic skeleton. Examples in this analysis include in the reduce → instance of → but fails to be completely type safe and NVidia GPGPUs → instance of → possibly equipped with computing accelerators. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| in the reduce | instance of | but fails to be completely type safe | 0.80 | text |
| NVidia GPGPUs | instance of | possibly equipped with computing accelerators | 0.80 | text |
| Xeon Phi | instance of | possibly equipped with computing accelerators | 0.80 | text |
| Tilera TILE64 | instance of | possibly equipped with computing accelerators | 0.80 | text |
| Intel TBB | instance of | state-of-the-art parallel programming frameworks | 0.80 | text |
| OpenMP | instance of | state-of-the-art parallel programming frameworks | 0.80 | text |
| Cilk | instance of | state-of-the-art parallel programming frameworks | 0.80 | text |
| etc.HDCHigher-order Divide | instance of | state-of-the-art parallel programming frameworks | 0.80 | text |
| Conquer | instance of | state-of-the-art parallel programming frameworks | 0.80 | text |
| farm | instance of | remotely accessibly via Web Services.JaSkelJaSkel is a Java-based skeleton framework providing skeletons | 0.80 | text |
| pipe | instance of | remotely accessibly via Web Services.JaSkelJaSkel is a Java-based skeleton framework providing skeletons | 0.80 | text |
| heartbeat | instance of | remotely accessibly via Web Services.JaSkelJaSkel is a Java-based skeleton framework providing skeletons | 0.80 | text |
The concept neighborhoods around Algorithmic skeleton bring nearby vocabulary together. In this analysis, examples include Patterns, Programming and Language. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Algorithmic skeleton, one of the stronger structural bridges in this analysis connects Algorithmic skeleton with Frameworks and libraries. 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 Algorithmic skeleton to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Works & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Algorithmic skeleton · EN edition · Analysis: TopicsToTalkAbout