Parallel genetic algorithm for N‐Queens problem based on message passing interface‐compute unified device architecture

2020 ◽  
Vol 36 (4) ◽  
pp. 1621-1637
Author(s):  
Cao Jianli ◽  
Chen Zhikui ◽  
Wang Yuxin ◽  
Guo He
Keyword(s):  
Genetic Algorithm ◽  
Message Passing ◽  
Message Passing Interface ◽  
Compute Unified Device Architecture ◽  
Parallel Genetic Algorithm ◽  
Device Architecture

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2016 ◽  
pp. 1071-1084
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
Vol 6 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Distribution Method ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Scalable critical-path analysis and optimization guidance for hybrid MPI-CUDA applications

2016 ◽  
Vol 31 (6) ◽  
pp. 485-498 ◽  
Author(s):  
Felix Schmitt ◽  
Robert Dietrich ◽  
Guido Juckeland
Keyword(s):  
Message Passing ◽  
Message Passing Interface ◽  
Critical Path ◽  
Heterogeneous Systems ◽  
Program Execution ◽  
Compute Unified Device Architecture ◽  
Device Architecture ◽  
Program Complexity ◽  
Execution Trace ◽  
Programming Interface

The use of accelerators in heterogeneous systems is an established approach in designing petascale applications. Today, Compute Unified Device Architecture (CUDA) offers a rich programming interface for GPU accelerators but requires developers to incorporate several layers of parallelism on both the CPU and the GPU. From this increasing program complexity emerges the need for sophisticated performance tools. This work contributes by analyzing hybrid MPI-CUDA programs for properties based on wait states, such as the critical path, a metric proven to identify application bottlenecks effectively. We developed a tool to construct a dependency graph based on an execution trace and the inherent dependencies of the programming models CUDA and Message Passing Interface (MPI). Thereafter, it detects wait states and attributes blame to responsible activities. Together with the property of being on the critical path, we can identify activities that are most viable for optimization. To evaluate the global impact of optimizations to critical activities, we predict the program execution using a graph-based performance projection. The developed approach has been demonstrated with suitable examples to be both scalable and correct. Furthermore, we establish a new categorization of CUDA inefficiency patterns ensuing from the dependencies between CUDA activities.

scholarly journals A PARALLEL GENETIC ALGORITHM FOR THE GEOMETRICALLY CONSTRAINED SITE LAYOUT PROBLEM WITH UNEQUAL-SIZE FACILITIES

2004 ◽  
Vol 04 (04) ◽  
pp. 375-400 ◽  
Author(s):  
HAIDAR M. HARMANANI ◽  
PIERRETTE P. ZOUEIN ◽  
AOUNI M. HAJAR
Keyword(s):  
Genetic Algorithm ◽  
Message Passing ◽  
Geometric Constraints ◽  
Computer Engineering ◽  
Parallel Genetic Algorithm ◽  
Layout Problem ◽  
Parallel Genetic Algorithms ◽  
Unequal Size ◽  
Site Layout ◽  
Geometrically Constrained

Parallel genetic algorithms techniques have been used in a variety of computer engineering and science areas. This paper presents a parallel genetic algorithm to solve the site layout problem with unequal-size and constrained facilities. The problem involves coordinating the use of limited space to accommodate temporary facilities subject to geometric constraints. The problem is characterised by affinity weights used to model transportation costs between facilities, and by geometric constraints between relative positions of facilities on site. The algorithm is parallelised based on a message passing SPMD architecture using parallel search and chromosomes migration. The algorithm is tested on a variety of layout problems to illustrate its performance. In specific, in the case of: (1) loosely versus tightly constrained layouts with equal levels of interaction between facilities, (2) loosely versus tightly packed layouts with variable levels of interactions between facilities, and (3) loosely versus tightly constrained layouts. Favorable results are reported.

Structural Optimization of an Optical 90 Degree Hybrid Based on a Weakly-guided 4x4 Multimode Interference Coupler Using a Parallelized Real-coded Micro-genetic Algorithm

2021 ◽  
Vol 36 (5) ◽  
pp. 526-532
Author(s):  
Takashi Yasui ◽  
Jun-ichiro Sugisaka ◽  
Koichi Hirayama
Keyword(s):  
Genetic Algorithm ◽  
Message Passing ◽  
Message Passing Interface ◽  
Phase Error ◽  
Beam Propagation Method ◽  
Optimization Process ◽  
Multimode Interference ◽  
Geometrical Parameters ◽  
Mmi Coupler ◽  
Micro Genetic Algorithm

The optimal design of a 4x4 multimode interference (MMI) coupler as an optical 90° hybrid based on a weakly-guided optical waveguide was considered. Seven geometrical parameters of a 4x4 MMI coupler were optimized by a real-coded micro-genetic algorithm, and parallelized using a message-passing interface. The beam-propagation method was used to evaluate the fitness of the MMI coupler in the optimization process. The optimized 4x4 MMI coupler showed a common-mode rejection ratio greater than 28.9 dBe and a phase error less than 2.52° across a wavelength range of 1520 to 1580 nm, which satisfied typical system requirements. The optimization process was executed on a Beowulf-style cluster comprising five identical PCs, and its parallel efficiency was 0.78.

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