Mapping stream programs onto multicore platforms by local search and genetic algorithm

This paper presents a number of novel metaheuristic approaches that can efficiently map stream graphs on multicores. A stream graph consists of a set of actors performing different functions communicating through edges. Orchestrating stream graphs on multicores can be formulated as an Integer Linear...

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Vydané v:Computer languages, systems & structures Ročník 46; s. 182 - 205
Hlavní autori: Farhad, S.M., Nayeem, Muhammad Ali, Rahman, Md. Khaledur, Rahman, M. Sohel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.11.2016
Predmet:
Algorithms
Compiler optimization
Genetic algorithm
Genetic algorithms
Graphs
Heuristic methods
Hybrid genetic algorithm
Local search operator
Mathematical models
Metaheuristics
Optimization
Parallel programming
Searching
Stream programming
Streams
Stream programming
Hybrid genetic algorithm
Local search operator
Parallel programming
Metaheuristics
Genetic algorithm
Compiler optimization
ISSN:1477-8424, 1873-6866
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Shrnutí:This paper presents a number of novel metaheuristic approaches that can efficiently map stream graphs on multicores. A stream graph consists of a set of actors performing different functions communicating through edges. Orchestrating stream graphs on multicores can be formulated as an Integer Linear Programming (ILP) problem but ILP solver takes exponential time to provide an optimal solution. We propose metaheuristic algorithms to achieve near optimal solutions within a reasonable amount of time. We employ six different variants of the Hill-Climbing (HC) algorithm employing different tweak operators that produce excellent result extremely quickly. We also propose six different variants of Genetic Algorithm (GA) to examine how effective these variants can be in escaping the local optima. We finally combine HC and GA techniques (which is also known as ‘memetic algorithm’) to produce hybrid techniques that outperform the individual performance of HC and GA techniques. We compare our results with the results generated by the CPLEX optimization tool. Our best technique has achieved a geometric mean speedup of 7.42× across a range of StreamIt benchmarks on an eight-core processor.
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ISSN:1477-8424
1873-6866
DOI:10.1016/j.cl.2016.08.007