Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework

General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe...

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Vydáno v:Genetic programming and evolvable machines Ročník 10; číslo 4; s. 391 - 415
Hlavní autoři: Munawar, Asim, Wahib, Mohamed, Munetomo, Masaharu, Akama, Kiyoshi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.12.2009
Témata:
Algorithms
Architecture
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computation
Computer Science
Electrical Engineering
Genetic algorithms
Genetics
Interpreters
Operators
Original Paper
Parallel processing
Programming Languages
Programming Techniques
Searching
Software Engineering/Programming and Operating Systems
Single instruction multiple threads (SIMT)
Genetic algorithm (GA)
General-purpose computing on graphics processing unit (GPGPU)
Single instruction multiple data (SIMD)
Compute unified device architecture (CUDA)
MAXimum SATisfiability problem (MAX-SAT)
ISSN:1389-2576, 1573-7632
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Popis
Shrnutí:General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe the challenges and design choices involved in parallelizing a hybrid of Genetic Algorithm (GA) and Local Search (LS) to solve MAXimum SATisfiability (MAX-SAT) problem on a state-of-the-art nVidia Tesla GPU using nVidia Compute Unified Device Architecture (CUDA). MAX-SAT is a problem of practical importance and is often solved by employing metaheuristics based search methods like GAs and hybrid of GA with LS. Almost all the parallel GAs (pGAs) designed in the last two decades were designed for either clusters or MPPs. Unfortunately, very little research is done on the implementation of such algorithms over commodity graphics hardware. GAs in their simple form are not suitable for implementation over the Single Instruction Multiple Thread (SIMT) architecture of a GPU, and the same is the case with conventional LS algorithms. In this paper we explore different genetic operators that can be used for an efficient implementation of GAs over nVidia GPUs. We also design and introduce new techniques/operators for an efficient implementation of GAs and LS over such architectures. We use nVidia Tesla C1060 to perform several numerical tests and performance measurements and show that in the best case we obtain a speedup of 25×. We also discuss the effects of different optimization techniques on the overall execution time.
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ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-009-9091-4