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Automatic parallelization

Automatic parallelization, also auto-parallelization or autoparallelization, is a compiler optimization in which a compiler or other software tool transforms sequential program code so that some of its operations can execute in parallel. The generated program may use multiple processor cores or hardware threads, vector instructions, or other forms of…

History, Compiler techniques & Forms of parallelism

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History

Vector processorsFortranDavid KuckUniversity of Illinois Urbana-ChampaignKen Kennedy
14 related topics

Compiler techniques

Data dependencePoints-to analysis
2 related topics

Forms of parallelism

Pipeline parallelismSoftware pipelining
2 related topics

Programmer-assisted parallelization

OpenMPHigh Performance Fortran
2 related topics

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Overview

History

Compiler techniques

Forms of parallelism

Programmer-assisted parallelization

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Map overview Semantic statistics

Automatic parallelization

Nodes34
Edges33
Triples37
Avg. degree1.94
Density0.058824
Components1

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Automatic parallelization

Top relations

related to history · 12
Automatic parallelization → David Kuck, During, Fortran, IBM, Illinois Urbana-Champaign, Ken Kennedy, One, Parafrase, Research, Rice University, University, Work
related to Programmer-assisted parallelization · 5
Automatic parallelization → Because, Fortran, It, OpenMP, The OpenMP
related to Implementations and research systems · 4
Automatic parallelization → Automatic, GCC, GCC's-ftree-parallelize-loopsoptimization, The
related to Challenges and limitations · 2
Automatic parallelization → Common, Fully
related to Compiler techniques · 1
Automatic parallelization → Automatic

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Important terminology

parallel compiler parallelization execution program automatic analysis loop dependence operations transformations parallelism iterations work memory may multiple behavior transformation execute

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
dependence analysisinstance ofThis involves analyses0.80text
alias analysisinstance ofThis involves analyses0.80text
data-flow analysisinstance ofThis involves analyses0.80text
together with program transformations that expose or increase usable parallelism.Loops have historically been an important target for automatic parallelizationinstance ofThis involves analyses0.80text
particularly in numerical programs with regular array accessesinstance ofThis involves analyses0.80text
cache localityinstance ofprivatization and transformations that modify or eliminate dependences.Transformations may interact with other optimization goals0.80text
vectorizationinstance ofprivatization and transformations that modify or eliminate dependences.Transformations may interact with other optimization goals0.80text
so increasing the amount of exposed parallelism does not by itself guarantee improved execution time.Profitability analysisAfter determining that parallel execution is legalinstance ofprivatization and transformations that modify or eliminate dependences.Transformations may interact with other optimization goals0.80text
a compiler can estimate whether it is worthwhileinstance ofprivatization and transformations that modify or eliminate dependences.Transformations may interact with other optimization goals0.80text
non-overlapping memory regions for a particular execution when static analysis alone cannot prove theminstance ofRuntime checks can establish properties0.80text
so increasing the amount of exposed parallelism does not by itself guarantee improved execution timeinstance ofprivatization and transformations that modify or eliminate dependences.Transformations may interact with other optimization goals0.80text
software pipelining similarly reorganize operations from different loop iterations so that multiple stages of computation overlap in executioninstance ofproducing pipeline parallelism.Compiler techniques0.80text

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