Symbolic Scheduling of Parameterized Task Graphs on Parallel Machines
Identifieur interne : 009E23 ( Main/Exploration ); précédent : 009E22; suivant : 009E24Symbolic Scheduling of Parameterized Task Graphs on Parallel Machines
Auteurs : Michel Cosnard [France] ; Emmanuel Jeannot [France] ; Tao Yang [États-Unis]Source :
- Combinatorial Optimization [ 1388-3011 ]
Abstract
Abstract: In this chapter we address the problem of allocating parallel tasks on a distributed memory machine for coarse-grain applications represented by parameterized task graphs (PTG). A PTG is a new computation model for representing directed acyclic task graphs (DAG) symbolically. The size of a PTG is independent of the problem size and its parameters can be instantiated at run time. Parameterindependent optimization is important for exploiting non-static parallelism in scientific computing programs with varying problem sizes and the previous DAG scheduling algorithms are not able to handle such cases. We present and study a symbolic scheduling algorithm called SLC (Symbolic Linear Clustering) which derives task clusters from a PTG using affine piecewise mapping functions and then evenly assigns clusters to processors. Our experimental results show that the proposed method is effective for a number of compute-intensive problems in scientific applications.
Url:
DOI: 10.1007/978-1-4757-3155-2_9
Affiliations:
- France, États-Unis
- Aquitaine, Californie, Grand Est, Lorraine (région), Nouvelle-Aquitaine
- Talence, Villers les Nancy
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UCSB, Engr Building I, 93106, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">États-Unis</country></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Combinatorial Optimization</title><idno type="ISSN">1388-3011</idno><idno type="ISSN">1388-3011</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1388-3011</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: In this chapter we address the problem of allocating parallel tasks on a distributed memory machine for coarse-grain applications represented by parameterized task graphs (PTG). A PTG is a new computation model for representing directed acyclic task graphs (DAG) symbolically. The size of a PTG is independent of the problem size and its parameters can be instantiated at run time. Parameterindependent optimization is important for exploiting non-static parallelism in scientific computing programs with varying problem sizes and the previous DAG scheduling algorithms are not able to handle such cases. We present and study a symbolic scheduling algorithm called SLC (Symbolic Linear Clustering) which derives task clusters from a PTG using affine piecewise mapping functions and then evenly assigns clusters to processors. 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