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Explore the main themes, entities and connections around Parallel computing. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Explore this topic
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
History
20 related topics
Background
42 related topics
Granularity
21 related topics
Hardware
27 related topics
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Topics to explore
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.Browse the full topic structure. Each item opens a new analysis centered on that subject.
Overview
- Computation Computing
- Processes Process (computing)
- Bit-level Bit-level parallelism
- Instruction-level Instruction-level parallelism
- Data Data parallelism
- Task parallelism
- High-performance computing
- Frequency scaling
- Computer architecture
- Multiple CPU cores Multi-core processor
- Computer science
- Threads Thread (computing)
- Multi-processor Symmetric multiprocessing
- Processing elements Processing element
- Clusters Computer cluster
- MPPs Massively parallel (computing)
- Grids Grid computing
- Parallel algorithms Parallel algorithm
- Sequential Sequential algorithm
- Software bugs Software bug
- Race conditions Race condition
- Communication Computer networking
- Synchronization Synchronization (computer science)
- Upper bound
- Speed-up Speedup
- Amdahl's law
- Processing units Central processing unit
- Superscalar
- Execution units Execution unit
- IBM
Background
- Computer software
- Serial computation
- Algorithm
- Engineering sciences
- Meteorology
- Computer performance
- Runtime Run time (program lifecycle phase)
- Compute-bound CPU bound
- Capacitance
- Voltage
- Tejas and Jayhawk
- Desktop computers
- Servers Server (computing)
- ARM's big.LITTLE ARM big.LITTLE
- Operating system
- Inter-process communication
- Gustafson's law
- Universal Scalability Law Neil J. Gunther
- Data dependencies Data dependency
- Critical path Critical path method
- Semaphore Semaphore (programming)
- Barrier Barrier (computer science)
- Fibers Fiber (computer science)
- Object Object (computer science)
- Resource Resource management (computing)
- Variable Variable (programming)
- Lock Lock (computer science)
- Mutual exclusion
- Critical section
- Locked out Software lockout
Granularity
- Very-large-scale integration
- Computer word size Word (data type)
- 8-bit 8-bit computing
- 16-bit 16-bit computing
- Integers Integer
- Carry bit
- 4-bit 4-bit computing
- X86-64
- 64-bit 64-bit computing
- Instruction per clock cycle Instructions per cycle
- Re-ordered Out-of-order execution
- Instruction pipelines Instruction pipelining
- RISC
- Pentium 4
- Scoreboarding
- Tomasulo algorithm
- Register renaming
- Vectorization Automatic vectorization
- Loop unrolling Loop unwinding
- Parallelism Parallelism (computing)
- Inline code
Hardware
- Shared memory Shared memory (interprocess communication)
- Address space
- Distributed memory
- Distributed shared memory
- Memory virtualization
- Supercomputers
- PGAS Partitioned global address space
- Infiniband
- Burst buffer
- Non-volatile memory
- Latency Memory latency
- Bandwidth Bandwidth (computing)
- Uniform memory access
- Non-uniform memory access
- LPUs Groq
- Caches CPU cache
- Cache coherency
- Bus snooping Bus sniffing
- Multiplexed Multiplexing
- Crossbar switch
- Topologies Network topology
- Star Star network
- Ring Ring network
- Tree Tree (graph theory)
- Hypercube Hypercube graph
- N-dimensional mesh Mesh networking
- Routing
Software
- Libraries Library (computing)
- APIs Application programming interface
- Parallel programming models Parallel programming model
- Algorithmic skeletons Algorithmic skeleton
- Message passing
- POSIX Threads
- OpenMP
- Message Passing Interface
- Future concept Futures and promises
- OpenHMPP
- Remote procedure calls Remote procedure call
- Compute kernels Compute kernel
- Compute shaders Compute shader
- Automatic parallelization
- Compiler
- Explicitly parallel Explicit parallelism
- Partially implicit Implicit parallelism
- Directives Directive (programming)
- SISAL
- Haskell
- SequenceL
- Mean time between failures
- Application checkpointing
- Core dump
Algorithmic methods
- Bioinformatics
- Protein folding
- Sequence analysis
- Cooley–Tukey fast Fourier transform Cooley–Tukey FFT algorithm
- N-body problems N-body problem
- Barnes–Hut simulation
- Structured grid Regular grid
- Lattice Boltzmann methods
- Unstructured grid
- Finite element analysis
- Monte Carlo method
- Combinational logic
- Brute-force cryptographic techniques Brute force attack
- Graph traversal
- Sorting algorithms Sorting algorithm
- Dynamic programming
- Branch and bound
- Graphical models Graphical model
- Hidden Markov models Hidden Markov model
- Bayesian networks Bayesian network
- HBJ model
- Finite-state machine
- Optimization problems Optimization problem
- Genetic algorithms Genetic algorithm
- Simulated annealing
- Particle methods Particle method
- Particle-in-cell
- Smoothed particle hydrodynamics
- Constraint satisfaction problems (CSPs) Constraint satisfaction problem
- Data mining
Fault tolerance
- Fault-tolerant computer systems Fault-tolerant computer system
- Lockstep Lockstep (computing)
- Redundancy Redundancy (engineering)
- Error detection
- Error correction
History
- Luigi Federico Menabrea
- Analytic Engine
- Charles Babbage
- Compagnie des Machines Bull
- Gamma 60 Bull Gamma 60
- Fork-join model Fork–join model
- John Cocke John Cocke (computer scientist)
- Daniel Slotnick
- Burroughs Corporation
- Honeywell
- Multics
- C.mmp
- Carnegie Mellon University
- Gate delay Propagation delay
- Control unit
- Lawrence Livermore National Laboratory
- US Air Force
- ILLIAC IV
- Vector processing Vector processor
- Cray-1
Biological brain as massively parallel computer
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.
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.Parallel computing
Nodes298
Edges297
Triples88
Avg. degree1.99
Density0.006711
Components1
How this topic connects Entity context
Quick relationship hints grouped by predicate. Useful for spotting recurring semantic connections around the current entity.See the strongest relationship patterns around the current topic before diving into the raw triples.
Parallel computing
Top relations
has method · 23
Parallel computing → As, Barnes, Bayesian, Common, Constraint, Cooley, CSPs, Data, Dense, Dynamic, Fields, Fourier, Graph, HBJ, Hut, Lattice Boltzmann, Markov, Monte Carlo, N-body, Particle
related to Further reading · 15
Parallel computing → Asynchronous, August, Baran, BIMNICS, Bio-Inspired Models, Boolean Satisfiability, Computing Systems, GIM International, Information, Network, Photogrammetry, Rodriguez, S2CID, Sechin, Villagra
related to Automatic parallelization · 11
Parallel computing → Automatic, Despite, FPGAs, Mainstream, Mitrion-C, Parallel Haskell, SequenceL, SISAL, SystemC, Verilog, VHDL
related to background · 8
Parallel computing → Historically, Only, Parallel, The, These, This, To, Traditionally
related to External links · 6
Parallel computing → Archive, Building Parallel Programs, Ian FosterInternet Parallel Computing, Introduction, Lawrence Livermore National Laboratory, Parallel ComputingDesigning
related to Fault tolerance · 4
Parallel computing → Although, Parallel, These, This
related to Disadvantages · 3
Parallel computing → Parallel, Specifically, Therefore
is a · 1
Parallel computing → type of computation in which many calculations or processes are carried out simultaneously
Important terminology Word statistics
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Important terminology
parallel computer computing parallelism processors program memory processor processing one multiple computers use distributed data threads programming instructions known system
Entity relationships Subject–Predicate–Object triples
Extracted RDF-like relationships with confidence and source. The table includes structured facts and lower-confidence contextual relations.| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Parallel computing | is a | type of computation in which many calculations or processes are carried out simultaneously | 0.90 | text |
| a single computer with multiple processors | instance of | The processing elements can be diverse and include resources | 0.80 | text |
| several networked computers | instance of | The processing elements can be diverse and include resources | 0.80 | text |
| specialized hardware | instance of | The processing elements can be diverse and include resources | 0.80 | text |
| or any combination of the above | instance of | The processing elements can be diverse and include resources | 0.80 | text |
| data persistence | instance of | primarily focusing on computational aspect and ignoring extrinsic factors | 0.80 | text |
| I/O operations | instance of | primarily focusing on computational aspect and ignoring extrinsic factors | 0.80 | text |
| and memory access overheads.Gustafson's law | instance of | primarily focusing on computational aspect and ignoring extrinsic factors | 0.80 | text |
| Universal Scalability Law give a more realistic assessment of the parallel performance.DependenciesUnderstanding data dependencies is fundamental in implementing parallel algorithms | instance of | primarily focusing on computational aspect and ignoring extrinsic factors | 0.80 | text |
| Universal Scalability Law give a more realistic assessment of the parallel performance | instance of | primarily focusing on computational aspect and ignoring extrinsic factors | 0.80 | text |
| PGAS | instance of | distributed shared memory space can be implemented using the programming model | 0.80 | text |
| Cerberus | instance of | utilized by protocols | 0.80 | text |
No triples extracted yet.
Related concept clusters Concept neighborhoods
Clusters of nearby vocabulary surrounding the topic. Scan them for adjacent concepts and language you may have missed.These clusters group vocabulary that occurs around closely connected concepts in the source material.
Connections between topic areas Semantic bridges
Bridge nodes connect otherwise separate parts of the map. Expand a row to inspect the topic groups on each side.Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.
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