Parallel partitioning for distributed systems using sequential assignment

This paper introduces a method to combine the advantages of both task parallelism and fine-grained co-design specialisation to achieve faster execution times than either method alone on distributed heterogeneous architectures. The method uses a novel mixed integer linear programming formalisation to...

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Vydané v:Journal of parallel and distributed computing Ročník 73; číslo 2; s. 207 - 219
Hlavní autori: Spacey, Simon, Luk, Wayne, Kuhn, Daniel, Kelly, Paul H.J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Inc 01.02.2013
Elsevier
Predmet:
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Heterogeneous computing
High-performance computing
Parallel partitioning
Sequential assignment
Software
Write-Only Architecture
Write-Only Architecture
Heterogeneous computing
High-performance computing
Parallel partitioning
Sequential assignment
Supercomputer
High performance
Parallel algorithm
Formal method
Partition method
Codesign
Task scheduling
Linear programming
Mixed integer programming
Distributed system
Distributed computing
Delay
Heterogeneity
Fine grain structure
Parallelism
Execution time
ISSN:0743-7315, 1096-0848
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Popis
Shrnutí:This paper introduces a method to combine the advantages of both task parallelism and fine-grained co-design specialisation to achieve faster execution times than either method alone on distributed heterogeneous architectures. The method uses a novel mixed integer linear programming formalisation to assign code sections from parallel tasks to share computational components with the optimal trade-off between acceleration from component specialism and serialisation delay. The paper provides results for software benchmarks partitioned using the method and formal implementations of previous alternatives to demonstrate both the practical tractability of the linear programming approach and the increase in program acceleration potential deliverable. ► A formal multi-level model for assigning parallel tasks to distributed hardware. ► Insights that allow optimal timing solutions to be found using standard solvers. ► Experimental results for a suite of real software benchmarks on the Axel cluster. ► Robust timing improvements over previous parallel approaches (up to 2.021 times faster).
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2012.09.019