scholarly journals Simulating Growth Kinetics in a Data-Parallel 3D Lattice Photobioreactor

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
A. V. Husselmann ◽  
K. A. Hawick
Keyword(s):  
Growth Kinetics ◽  
Visual Cues ◽  
Parallel Implementation ◽  
Small Scale ◽  
Complex Nature ◽  
Agent Based ◽  
Data Parallel ◽  
Graphical Processing Units ◽  
Isosurface Rendering ◽  
Graphical Processing

Though there have been many attempts to address growth kinetics in algal photobioreactors, surprisingly little have attempted an agent-based modelling (ABM) approach. ABM has been heralded as a method of practical scientific inquiry into systems of a complex nature and has been applied liberally in a range of disciplines including ecology, physics, social science, and microbiology with special emphasis on pathogenic bacterial growth. We bring together agent-based simulation with the Photosynthetic Factory (PSF) model, as well as certain key bioreactor characteristics in a visual 3D, parallel computing fashion. Despite being at small scale, the simulation gives excellent visual cues on the dynamics of such a reactor, and we further investigate the model in a variety of ways. Our parallel implementation on graphical processing units of the simulation provides key advantages, which we also briefly discuss. We also provide some performance data, along with particular effort in visualisation, using volumetric and isosurface rendering.

Efficient Graph Component Labeling on Hybrid CPU and GPU Platforms

2014 ◽  
Vol 596 ◽  
pp. 276-279
Author(s):  
Xiao Hui Pan
Keyword(s):  
High Performance ◽  
General Purpose ◽  
Gpu Programming ◽  
Data Parallel ◽  
Graphical Processing Units ◽  
Architectural Features ◽  
Graph Coloring Problem ◽  
Graphical Processing ◽  
And Performance ◽  
Performance Results

Graph component labeling, which is a subset of the general graph coloring problem, is a computationally expensive operation in many important applications and simulations. A number of data-parallel algorithmic variations to the component labeling problem are possible and we explore their use with general purpose graphical processing units (GPGPUs) and with the CUDA GPU programming language. We discuss implementation issues and performance results on CPUs and GPUs using CUDA. We evaluated our system with real-world graphs. We show how to consider different architectural features of the GPU and the host CPUs and achieve high performance.

COMPARISON OF PARALLEL PARTICLE SWARM OPTIMIZERS FOR GRAPHICAL PROCESSING UNITS AND MULTICORE PROCESSORS

2013 ◽  
Vol 12 (01) ◽  
pp. 1350006 ◽  
Author(s):  
VINCENT ROBERGE ◽  
MOHAMMED TARBOUCHI
Keyword(s):  
Parallel Implementation ◽  
Multicore Processors ◽  
Statistical Significance ◽  
Particle Swarm ◽  
Graphical Processing Units ◽  
Processing Power ◽  
Time Harmonic ◽  
Power Inverters ◽  
Graphical Processing ◽  
Nvidia Gpu

In this paper, we present a parallel implementation of the particle swarm optimization (PSO) on graphical processing units (GPU) using CUDA. By fully utilizing the processing power of graphic processors, our implementation (CUDA-PSO) provides a speedup of 167× compared to a sequential implementation on CPU. This speedup is significantly superior to what has been reported in recent papers and is achieved by four optimizations we made to better adapt the parallel algorithm to the specific architecture of the NVIDIA GPU. However, because today's personal computers are usually equipped with a multicore CPU, it may be unfair to compare our CUDA implementation to a sequential one. For this reason, we implemented a parallel PSO for multicore CPUs using MPI (MPI-PSO) and compared its performance against our CUDA-PSO. The execution time of our CUDA-PSO remains 15.8× faster than our MPI-PSO which ran on a high-end 12-core workstation. Moreover, we show with statistical significance that the results obtained using our CUDA-PSO are of equal quality as the results obtained by the sequential PSO or the MPI-PSO. Finally, we use our parallel PSO for real-time harmonic minimization of multilevel power inverters with 20 DC sources while considering the first 100 harmonics and show that our CUDA-PSO is 294× faster than the sequential PSO and 32.5× faster than our parallel MPI-PSO.

A practical implementation of acoustic full waveform inversion on graphical processing units

2015 ◽  
Vol 6 (2) ◽  
pp. 5-16 ◽  
Author(s):  
Sergio Alberto Abreo Carrillo ◽  
Ana B. Ramirez ◽  
Oscar Reyes ◽  
David Leonardo Abreo-Carrillo ◽  
Herling González Alvarez
Keyword(s):  
Waveform Inversion ◽  
Practical Implementation ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Graphical Processing Units ◽  
Graphical Processing

Using GPUs to compute fast Fourier transforms for crystal structure solution and refinement

2013 ◽  
Vol 46 (3) ◽  
pp. 594-600 ◽  
Author(s):  
ElSayed Mohamed Shalaby ◽  
Miguel Afonso Oliveira
Keyword(s):  
Crystal Structure ◽  
Fourier Transformation ◽  
Fourier Transforms ◽  
Fast Fourier Transformation ◽  
Parallel Calculations ◽  
The Past ◽  
Graphical Processing Units ◽  
Software Algorithms ◽  
Structure Solution ◽  
Graphical Processing

In the past few years, new hardware tools have become available for computing using the graphical processing units (GPUs) present in modern graphics cards. These GPUs allow efficient parallel calculations with a much higher throughput than microprocessors. In this work, fast Fourier transformation calculations used inSIR2011software algorithms have been carried out using the power of the GPU, and the speed of the calculations has been compared with that achieved using normal CPUs.

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