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Hypre: Standards & Overview

The Parallel High Performance Preconditioners (hypre) is a library of routines for scalable (parallel) solution of linear systems. The built-in BLOPEX package in addition allows solving eigenvalue problems. The main strength of Hypre is availability of high performance parallel multigrid preconditioners for both structured and unstructured grid problems.

Language: English [EN]
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Hypre topic overview

The analysis highlights Standards and Overview as prominent areas in the source structure around Hypre.

Related topics
12
Source areas
1
Connected nodes
13
Extracted relationships
9
Concept neighborhoods
13
Bridge connections
13

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 · 12 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.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Available in
C (main language), C++, FORTRAN
License
LGPL (version 2.1)
Operating system
Linux, Unix
Repository
github.com/hypre-space/hypre
Stable release
2.11.2 / 2017/03/13
Type
High-performance Parallel Software for linear systems and eigenvalue problems

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

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 Hypre connects Entity context

The extracted context around Hypre shows recurring relationship patterns in the source. For example, Hypre → BLOPEX, Parallel Another extracted example is Hypre → C (main language), C++, FORTRAN. Use these groups to spot repeated connection types before inspecting the individual relationships.

Hypre

Top relations

related to Features · 2
Hypre → BLOPEX, Parallel
Available in · 1
Hypre → C (main language), C++, FORTRAN
License · 1
Hypre → LGPL (version 2.1)
Operating system · 1
Hypre → Linux, Unix
Repository · 1
Hypre → github.com/hypre-space/hypre
Stable release · 1
Hypre → 2.11.2 / 2017/03/13
Type · 1
Hypre → High-performance Parallel Software for linear systems and eigenvalue problems
Website · 1
Hypre → https://computing.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods

Important terminology

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

Important terminology

preconditioners parallel scalable linear problems high performance interface eigenvalue blopex systems main petsc built-in features routines multigrid real double-precision library

Hypre relationships Subject–Predicate–Object triples

TTTA extracted 9 structured relationships around Hypre. Examples in this analysis include Hypre → Available in → C (main language), C++, FORTRAN and Hypre → License → LGPL (version 2.1). The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
HypreAvailable inC (main language), C++, FORTRAN1.00infobox
HypreLicenseLGPL (version 2.1)1.00infobox
HypreOperating systemLinux, Unix1.00infobox
HypreRepositorygithub.com/hypre-space/hypre1.00infobox
HypreStable release2.11.2 / 2017/03/131.00infobox
HypreTypeHigh-performance Parallel Software for linear systems and eigenvalue problems1.00infobox
HypreWebsitehttps://computing.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods1.00infobox
Hyprerelated to FeaturesParallel0.60section
Hyprerelated to FeaturesBLOPEX0.60section

Related concept clusters Concept neighborhoods

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

  • Hypre
    • Preconditioners
    • Linear
    • Parallel
    • Scalable
    • Interface
    • Main
    • Performance
    • Petsc
    • Systems
    • Problems
    • Library
    • Message
  • hypre
    • Preconditioners
    • Linear
    • Parallel
    • Scalable
    • Interface
    • Main
    • Performance
    • Petsc
    • Systems
    • Problems
    • Library
    • Message
  • parallel
    • Preconditioners
    • Main
    • Performance
    • Systems
    • Linear
    • Problems
    • Scalable
    • Hypre
    • Availability
    • Features
    • Grid
    • Library
  • preconditioners
    • Main
    • Petsc
    • Systems
    • Problems
    • Scalable
    • Availability
    • Features
    • Grid
    • Multigrid
    • Routines
    • Solution
    • Strength
  • blopex
    • Built-in
    • Eigenvalue
    • Problems
    • Addition
    • Allows
    • Features
    • Package
    • Solving
    • Main
    • Petsc
    • Systems
    • Linear
  • eigenvalue
    • Problems
    • Features
    • Package
    • Solving
    • Main
    • Petsc
    • Systems
    • Linear
    • Parallel
    • Scalable
    • Preconditioners
    • Hypre
  • scalable
    • Systems
    • Features
    • Solution
    • Blopex
    • Built-in
    • Eigenvalue
    • Main
    • Petsc
    • Problems
  • message passing interface
    • Message-passing
    • Mpi
    • Passing
    • Standard
    • Uses
    • Interface
    • Message
    • Petsc
    • Preconditioners

Connections between topic areas Semantic bridges

Bridges highlight paths between different parts of the Hypre map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Map overview Semantic statistics

Hypre

Nodes14
Edges13
Triples9
Avg. degree1.86
Density0.142857
Components1

Source & methodology

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

Source: Wikipedia — Hypre · EN edition · Analysis: TopicsToTalkAbout

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