About: Scalable parallelism

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Software is said to exhibit scalable parallelism if it can make use of additional processors to solve larger problems,i.e. this term refers to software for which Gustafson's law holds.Consider a program whose execution time is dominated by one or more loops,each of that updates every element of an array ---for example, the following finite difference heat equation stencil calculation: for t := 0 to T do for i := 1 to N-1 do new(i) := (A(i-1) + A(i) + A(i) + A(i+1)) * .25 // explicit forward-difference with R = 0.25 end for i := 1 to N-1 do A(i) := new(i) endend

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rdfs:label	Scalable parallelism (en)
rdfs:comment	Software is said to exhibit scalable parallelism if it can make use of additional processors to solve larger problems,i.e. this term refers to software for which Gustafson's law holds.Consider a program whose execution time is dominated by one or more loops,each of that updates every element of an array ---for example, the following finite difference heat equation stencil calculation: for t := 0 to T do for i := 1 to N-1 do new(i) := (A(i-1) + A(i) + A(i) + A(i+1)) * .25 // explicit forward-difference with R = 0.25 end for i := 1 to N-1 do A(i) := new(i) endend (en)
sameAs	Scalable parallelism
dbp:wikiPageUsesTemplate	dbt:Cite_web dbt:Reflist
Subject	Articles with example pseudocode Analysis of parallel algorithms
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Scalable_parallelism?oldid=939094044&ns=0
Wikipage page ID	23048148 (xsd:integer)
page length (characters) of wiki page	3314 (xsd:nonNegativeInteger)
Wikipage revision ID	939094044 (xsd:integer)
has abstract	Software is said to exhibit scalable parallelism if it can make use of additional processors to solve larger problems,i.e. this term refers to software for which Gustafson's law holds.Consider a program whose execution time is dominated by one or more loops,each of that updates every element of an array ---for example, the following finite difference heat equation stencil calculation: for t := 0 to T do for i := 1 to N-1 do new(i) := (A(i-1) + A(i) + A(i) + A(i+1)) * .25 // explicit forward-difference with R = 0.25 end for i := 1 to N-1 do A(i) := new(i) endend In the above code, we can execute all iterations of each "i" loop concurrently,i.e., turn each into a parallel loop.In such cases,it is often possible to make effective use of twice as many processors for a problem of array size 2Nas for a problem of array size N.As in this example, scalable parallelism is typically a form of data parallelism.This form of parallelism is often the target of automatic parallelization of loops. Distributed computing systems and non-uniform memory access architecturesare typically the most easily scaled to large numbers of processors,and thus would seem a natural target for software that exhibits scalable parallelism.However, applications with scalable parallelism may not have parallelism ofsufficiently coarse grainto run effectively on such systems (unless the software is embarrassingly parallel).In our example above, the second "i" loop is embarrassingly parallel,but in the first loop each iteration requires results produced in several prior iterations.Thus, for the first loop, parallelization may involve extensive communication or synchronization among processors,and thus only result in a net speedup if such interactions have very low overhead,or if the code can be transformed to resolve this issue (i.e., by combined scalable locality/scalable parallelism optimization). (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Scalable_parallelism
is Wikipage redirect of	Scalable Parallelism
is Link from a Wikipage to another Wikipage of	Fortran Frameworks supporting the polyhedral model Automatic parallelization Automatic parallelization tool Kunle Olukotun Data parallelism Gustafson's law Loop optimization

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