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Optimizing Shape Design with Distributed Parallel Genetic Programming on GPUs [chapter]

Simon Harding, W. Banzhaf
2012 Studies in Computational Intelligence  
We present work on optimized shape design using a technique from the area of Genetic Programming, self-modifying Cartesian Genetic Programming (SMCGP), to evolve shapes with specific criteria, such as  ...  Fitness evaluation of the genetic programming technique is accomplished through a custom implementation of a fluid dynamics solver running on graphics processing units (GPUs).  ...  Optimized Shape Design with SMCGP and CFD-GPU Optimized shape design is the optimization of shapes in order to minimize and/or maximize specific parameters of the shape.  ... 
doi:10.1007/978-3-642-28789-3_3 fatcat:c625ysjdlffe7m4nddrtgsgayu

Parallel Genetic Algorithms with GPU Computing [chapter]

John Runwei Cheng, Mitsuo Gen
2020 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing [Working Title]  
Designing a parallel algorithm on GPU is different fundamentally from designing one on CPU.  ...  Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing and logistic fields.  ...  Since massive threads are available on GPU, the way of designing a parallel program on GPU is quite different with the way of designing one on CPU.  ... 
doi:10.5772/intechopen.89152 fatcat:ktigfcnb35hpve7jtvwobuv3d4

Parallel Genetic Algorithms on Programmable Graphics Hardware [chapter]

Qizhi Yu, Chongcheng Chen, Zhigeng Pan
2005 Lecture Notes in Computer Science  
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems.  ...  Both fitness evaluation and genetic operations are implemented entirely with fragment programs executed on graphics processing unit in parallel.  ...  Acknowlegement This project is co-supported by 973 Program (No.2002CB312100) and Excellent Youth Teacher Program of MOE in China.  ... 
doi:10.1007/11539902_134 fatcat:w5k7vdkp25bm7pjlrnejyrq7wq

Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units

Hacer Yalim Keles
2018 Artificial intelligence for engineering design, analysis and manufacturing  
In this work, we propose a novel method to solve both problems; we treat shapes as they are and use a parallel particle swarm optimization-based algorithm to compute emergent parts.  ...  The second challenge is the relevant part searching algorithm that provides an extensive exploration of the design space–time efficiently.  ...  Hence, it is a scalable parallel programming model.  ... 
doi:10.1017/s089006041700052x fatcat:gmvvgqnc5vgl5p47qowngxlmvi

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

Asim Munawar, Mohamed Wahib, Masaharu Munetomo, Kiyoshi Akama
2009 Genetic Programming and Evolvable Machines  
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  ...  We also discuss the effects of different optimization techniques on the overall execution time.  ...  Acknowledgements We would like to thank Dalila Boughachi for her help, regarding the use of genetic algorithms to solve MAX-SAT problem.  ... 
doi:10.1007/s10710-009-9091-4 fatcat:oxxjtqnderdnhamtl4huueioxa

Finite elements, design optimization, and nondestructive evaluation: A review in magnetics, and future directions in GPU-based, element-by-element coupled optimization and NDE

S. Ratnajeevan H. Hoole, Victor U. Karthik, Sivamayam Sivasuthan, Arunasalam Rahunanthan, Ravi S. Thyagarajan, Paramsothy Jayakumar
2015 International journal of applied electromagnetics and mechanics  
GPUs have recently been introduced in finite element analysis but their memory limits are often not recognized and are critically limiting when parallelizing the several solutions required in optimization  ...  To overcome this limit, element-by-element finite element matrix processing is employed, making coupled problems practicable on GPUs. We overcome the memory limits faced by others.  ...  This number 26 is well above the number of parameters being usually optimized for designs so that such approaches are feasible for genetic algorithm parallelization on GPUs.  ... 
doi:10.3233/jae-140061 fatcat:dynyc5va4jgjzmlqvwhympqyna


2013 Procedia Computer Science  
GPU in a Parallel Distributed Environment C.F.P.  ...  for Engineering Design Optimization S.  ... 
doi:10.1016/s1877-0509(13)00604-2 fatcat:zlgxgtdrajc75bakqazfr6gqvu

Computational Biology as a Compelling Pedagogical Tool in Computer Science Education

Vijayalakshmi Saravanan, Anpalagan Alagan, Kshirasagar Naik
2020 The Journal of Computational Science Education  
In this paper, we introduce a novel course curriculum to teach highperformance, parallel and distributed computing to senior graduate students (PhD) in a hands-on setup through examples drawn from a wealth  ...  We introduce the concepts of parallel programming, algorithms and architectures and implementations via carefully chosen examples from computational biology.  ...  CUDA makes it simple for programmers with only a basic understanding of genetic algorithms to code their own genetic algorithms to run on NVIDIA GPUs.  ... 
doi:10.22369/issn.2153-4136/11/1/8 fatcat:e46lulvmifa4jglbtycjrqfyne

Porting and Optimizing Molecular Docking onto the SX-Aurora TSUBASA Vector Computer

2021 Supercomputing Frontiers and Innovations  
Specifically, we present our methodology for porting and optimizing AutoDock, a widely-used molecular docking program.  ...  Using a number of platform-specific code optimizations, we achieved executions on the SX-Aurora TSUBASA that are in average 3.6× faster than on modern 128-core CPU servers, and up to a certain extent,  ...  On GPUs, work-groups and work-items are executed in parallel by the fine-grain GPU streaming multiprocessors.  ... 
doi:10.14529/jsfi210202 fatcat:do3aqxpbvfhlth4vbklthbcptq

Advanced Aerostructural Optimization Techniques for Aircraft Design

Yingtao Zuo, Pingjian Chen, Lin Fu, Zhenghong Gao, Gang Chen
2015 Mathematical Problems in Engineering  
Traditional coupled aerostructural design optimization (ASDO) of aircraft based on high-fidelity models is computationally expensive and inefficient.  ...  The efficiency of the proposed optimization system can be improved greatly with the aid of GPU-accelerated RISM.  ...  Yingnan Guo, Qing Han, Zhengping Wang, and Jiechu Jiang of Northwestern Polytechnical University and Bo Zhang of Xi'an Jiaotong University for their assistance with this paper.  ... 
doi:10.1155/2015/753042 fatcat:o4s2xoex4jfr7ovlsp6xk75kwi

Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm

Ting Hu, Simon Harding, Wolfgang Banzhaf
2010 Genetic Programming and Evolvable Machines  
With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from parallelization techniques.  ...  In this article, we focus on the effect of variable population size on accelerating evolution in the context of a parallel evolutionary algorithm.  ...  APEA), designed to function with multiple CPUs and GPUs.  ... 
doi:10.1007/s10710-010-9105-2 fatcat:xr6ltxdqnjhjdfpeidk5xexqbm

CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA [chapter]

Zejun Zheng, Thuy-Diem Nguyen, Bertil Schmidt
2011 Lecture Notes in Computer Science  
On a single-GPU, CRiSPy achieves speedups of around two orders of magnitude compared to the sequential ESPRIT program for both the time-consuming pairwise genetic distance module and the whole processing  ...  To achieve high computational efficiency, we have designed massively parallel CUDA algorithms for pairwise k-mer distance and pairwise genetic distance computation.  ...  We have implemented three different versions of CRiSPy: 1. a multithreaded C++ program with OpenMP (CRiSPy MT) 2. a CUDA program running on a single-GPU (CRiSPy single-GPU) 3. a CUDA program running on  ... 
doi:10.1007/978-3-642-24855-9_4 fatcat:o6cxsmryejgvrb3orz3dbhrmii

GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space

Weihang Zhu, Yaohang Li
2010 Proceeding of the 2nd workshop on Bio-inspired algorithms for distributed systems - BADS '10  
Moreover, the DE-MCMC algorithm can efficiently take advantage of the massive-parallel, many-core architecture, where its implementation on GPU can achieve speedup of 14~35.  ...  problems with continuous variables.  ...  GPU Speedup Comparison on Benchmark Functions Our SIMT DE-MCMC program is implemented in Visual C++ 2005 with the CUDA environment for programming the GPU.  ... 
doi:10.1145/1809018.1809021 fatcat:ajq56cbiujb5dojllkxfjztbbu

A Survey of Medical Image Registration on Multicore and the GPU

Ramtin Shams, Parastoo Sadeghi, Rodney Kennedy, Richard Hartley
2010 IEEE Signal Processing Magazine  
distributed memory (DM), and nonuniform memory access (NUMA).  ...  The sections "Multi-CPU Implementations" and "Accelerator Implementations" are organized from the perspective of high-performance and parallel-computing with the registration problem embodied.  ...  Most serial programs can be parallelized on SMP architectures with minimal modification. The ease with which parallelization can be achieved, especially with OpenMP, can be deceiving.  ... 
doi:10.1109/msp.2009.935387 fatcat:zror76zxorgwdbrue2i5bqdyyq

Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Mathialakan Thavappiragasam, Aaron Scheinberg, Wael Elwasif, Oscar Hernandez, Ada Sedova
2020 2020 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC)  
The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented.  ...  Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer.  ...  This algorithm parallelizes well on GPUs, as each local optimization occurs independently from other members of the population.  ... 
doi:10.1109/p3hpc51967.2020.00009 fatcat:johyeat3uzgezfyef6odfmkhzu
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