Graphics processing unit acceleration of the island model genetic algorithm using the CUDA programming platform

2021 ◽  
Author(s):  
Dylan M. Janssen ◽  
Wayne Pullan ◽  
Alan Wee‐Chung Liew
Keyword(s):  
Genetic Algorithm ◽  
Graphics Processing Unit ◽  
Island Model ◽  
Processing Unit ◽  
Cuda Programming ◽  
Graphics Processing

scholarly journals GENETIC ALGORITHM ON GENERAL PURPOSE GRAPHICS PROCESSING UNIT: PARALLELISM REVIEW

2013 ◽  
Vol 03 (02) ◽  
pp. 492-497
Author(s):  
Umbarkar A.J. ◽  
◽  
Joshi M.S. ◽  
Rothe N.M. ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Processing Unit ◽  
Graphics Processing

scholarly journals On the issue of fuzzy timing estimations of the algorithms running at GPU and CPU architectures

2019 ◽  
Vol 135 ◽  
pp. 01082 ◽  
Author(s):  
Oleg Agibalov ◽  
Nikolay Ventsov
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Running Time ◽  
Average Value ◽  
Central Processing ◽  
Graphics Processing ◽  
Number Of Individuals ◽  
Gpu Architecture

We consider the task of comparing fuzzy estimates of the execution parameters of genetic algorithms implemented at GPU (graphics processing unit’ GPU) and CPU (central processing unit) architectures. Fuzzy estimates are calculated based on the averaged dependencies of the genetic algorithms running time at GPU and CPU architectures from the number of individuals in the populations processed by the algorithm. The analysis of the averaged dependences of the genetic algorithms running time at GPU and CPU-architectures showed that it is possible to process 10’000 chromosomes at GPU-architecture or 5’000 chromosomes at CPUarchitecture by genetic algorithm in approximately 2’500 ms. The following is correct for the cases under consideration: “Genetic algorithms (GA) are performed in approximately 2, 500 ms (on average), ” and a sections of fuzzy sets, with a = 0.5, correspond to the intervals [2, 000.2399] for GA performed at the GPU-architecture, and [1, 400.1799] for GA performed at the CPU-architecture. Thereby, it can be said that in this case, the actual execution time of the algorithm at the GPU architecture deviates in a lesser extent from the average value than at the CPU.

Collaborative Parallel Hybrid Metaheuristics on Graphics Processing Unit

2015 ◽  
Vol 14 (01) ◽  
pp. 1550002 ◽  
Author(s):  
Vincent Roberge ◽  
Mohammed Tarbouchi ◽  
Francis Okou
Keyword(s):  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Memory Systems ◽  
Parallel Execution ◽  
Island Model ◽  
Processing Unit ◽  
Hybrid Metaheuristics ◽  
Parallel Hybrid ◽  
Time Critical ◽  
Graphics Processing

Metaheuristics are nondeterministic optimization algorithms used to solve complex problems for which classic approaches are unsuitable. Despite their effectiveness, metaheuristics require considerable computational power and cannot easily be used in time critical applications. Fortunately, those algorithms are intrinsically parallel and have been implemented on shared memory systems and more recently on graphics processing units (GPUs). In this paper, we present highly efficient parallel implementations of the particle swarm optimization (PSO), the genetic algorithm (GA) and the simulated annealing (SA) algorithm on GPU using CUDA. Our approach exploits the parallelism at the solution level, follows an island model and allows for speedup up to 346× for different benchmark functions. Most importantly, we also present a strategy that uses the generalized island model to integrate multiple metaheuristics into a parallel hybrid solution adapted to the GPU. Our proposed solution uses OpenMP to heavily exploit the concurrent kernel execution feature of recent NVIDIA GPUs, allowing for the parallel execution of the different metaheuristics in an asynchronous manner. Asynchronous hybrid metaheuristics has been developed for multicore CPU, but never for GPU. The speedup offered by the GPU is far superior and key to the optimization of solutions to complex engineering problems.

Faster, more accurate, parallelized inversion for shape optimization in electroheat problems on a graphics processing unit (GPU) with the real-coded genetic algorithm

2015 ◽  
Vol 34 (1) ◽  
pp. 344-356 ◽  
Author(s):  
Victor U. Karthik ◽  
Sivamayam Sivasuthan ◽  
Arunasalam Rahunanthan ◽  
Ravi S. Thyagarajan ◽  
Paramsothy Jayakumar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Heat Distribution ◽  
Parallel Genetic Algorithm ◽  
Hyperthermia Treatment ◽  
Content Type ◽  
Real Coded Genetic Algorithm ◽  
Graphics Processing

Purpose – Inverting electroheat problems involves synthesizing the electromagnetic arrangement of coils and geometries to realize a desired heat distribution. To this end two finite element problems need to be solved, first for the magnetic fields and the joule heat that the associated eddy currents generate and then, based on these heat sources, the second problem for heat distribution. This two-part problem needs to be iterated on to obtain the desired thermal distribution by optimization. Being a time consuming process, the purpose of this paper is to parallelize the process using the graphics processing unit (GPU) and the real-coded genetic algorithm, each for both speed and accuracy. Design/methodology/approach – This coupled problem represents a heavy computational load with long wait-times for results. The GPU has recently been demonstrated to enhance the efficiency and accuracy of the finite element computations and cut down solution times. It has also been used to speedup the naturally parallel genetic algorithm. The authors use the GPU to perform coupled electroheat finite element optimization by the genetic algorithm to achieve computational efficiencies far better than those reported for a single finite element problem. In the genetic algorithm, coding objective functions in real numbers rather than binary arithmetic gives added speed and accuracy. Findings – The feasibility of the method proposed to reduce computational time and increase accuracy is established through the simple problem of shaping a current carrying conductor so as to yield a constant temperature along a line. The authors obtained a speedup (CPU time to GPU time ratio) saturating to about 28 at a population size of 500 because of increasing communications between threads. But this far better than what is possible on a workstation. Research limitations/implications – By using the intrinsically parallel genetic algorithm on a GPU, large complex coupled problems may be solved very quickly. The method demonstrated here without accounting for radiation and convection, may be trivially extended to more completely modeled electroheat systems. Since the primary purpose here is to establish methodology and feasibility, the thermal problem is simplified by neglecting convection and radiation. While that introduces some error, the computational procedure is still validated. Practical implications – The methodology established has direct applications in electrical machine design, metallurgical mixing processes, and hyperthermia treatment in oncology. In these three practical application areas, the authors need to compute the exciting coil (or antenna) arrangement (current magnitude and phase) and device geometry that would accomplish a desired heat distribution to achieve mixing, reduce machine heat or burn cancerous tissue. This process presented does it more accurately and speedily. Social implications – Particularly the above-mentioned application in oncology will alleviate human suffering through use in hyperthermia treatment planning in cancer treatment. The method presented provides scope for new commercial software development and employment. Originality/value – Previous finite element shape optimization of coupled electroheat problems by this group used gradient methods whose difficulties are explained. Others have used analytical and circuit models in place of finite elements. This paper applies the massive parallelization possible with GPUs to the inherently parallel genetic algorithm, and extends it from single field system problems to coupled problems, and thereby realizes practicable solution times for such a computationally complex problem. Further, by using GPU computations rather than CPU, accuracy is enhanced. And then by using real number rather than binary coding for object functions, further accuracy and speed gains are realized.

Fast iterative solvers for large compressed-sparse row linear systems on graphics processing unit

2015 ◽  
Vol 10 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Frédéric Magoulès ◽  
Abal-Kassim Cheik Ahamed ◽  
Roman Putanowicz
Keyword(s):  
Linear Systems ◽  
Graphics Processing Unit ◽  
Iterative Solvers ◽  
Processing Unit ◽  
Compressed Sparse Row ◽  
Graphics Processing

Implementing wide baseline matching algorithms on a graphics processing unit.

2007 ◽  
Author(s):  
Fredrick H. Rothganger ◽  
Kurt W. Larson ◽  
Antonio Ignacio Gonzales ◽  
Daniel S. Myers
Keyword(s):  
Graphics Processing Unit ◽  
Processing Unit ◽  
Wide Baseline Matching ◽  
Graphics Processing

scholarly journals Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

2021 ◽  
Vol 22 (10) ◽  
pp. 5212
Author(s):  
Andrzej Bak
Keyword(s):  
Molecular Conformation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Diverse Range ◽  
Current State ◽  
Gpu Clusters ◽  
Pharmacophore Hypothesis ◽  
Rising Power ◽  
Graphics Processing ◽  
Ligand Conformation

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Parallelization of Global Sequence Alignment on Graphics Processing Unit

2020 ◽  
Author(s):  
Kailash W. Kalare ◽  
Mohammad S. Obaidat ◽  
Jitendra V. Tembhurne ◽  
Chandrashekhar Meshram ◽  
Kuei-Fang Hsiao
Keyword(s):  
Sequence Alignment ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Graphics Processing
Sign in / Sign up

Export Citation Format

Share Document