The new SCIARA-fv3 numerical model and acceleration by GPGPU strategies

2016 ◽  
Vol 31 (2) ◽  
pp. 163-176 ◽  
Author(s):  
Davide Spataro ◽  
Donato D’Ambrosio ◽  
Giuseppe Filippone ◽  
Rocco Rongo ◽  
William Spataro ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Parallel Implementation ◽  
Cellular Automata Model ◽  
Performance Improvements ◽  
Device Architecture ◽  
Graphic Hardware ◽  
Significant Performance ◽  
Race Conditions ◽  
Cuda Architecture ◽  
Valid Solution

This paper presents the parallel implementation, using the Compute Unified Device Architecture (CUDA) architecture, of the SCIARA-fv3 Complex Cellular Automata model for simulating lava flows. The computational model is based on a Bingham-like rheology and both flow velocity and the physical time corresponding to a computational step have been made explicit. The parallelization design has involved, among other issues, the application of strategies that can avoid incorrect computation results due to race conditions and achieving the best performance and occupancy of the underlying available hardware. Two hardware types were adopted for testing different versions of the CUDA implementations of the SCIARA-fv3 model, namely the GTX 580 and GTX 680 graphic processors. Despite its computational complexity, carried out experiments of the model parallelization have shown significant performance improvements, confirming that graphic hardware can represent a valid solution for the implementation of Cellular Automata models.

Cellular Automata and GPGPU

2012 ◽  
Vol 4 (3) ◽  
pp. 30-47 ◽  
Author(s):  
Donato D’Ambrosio ◽  
Giuseppe Filippone ◽  
Rocco Rongo ◽  
William Spataro ◽  
Giuseppe A. Trunfio
Keyword(s):  
Cellular Automata ◽  
High Performance ◽  
Gpu Computing ◽  
Graphics Hardware ◽  
Data Sets ◽  
Problem Size ◽  
Performance Improvements ◽  
Graphic Data ◽  
Significant Performance ◽  
Graphical Processing

This paper presents an efficient implementation of the SCIARA Cellular Automata computational model for simulating lava flows using the Compute Unified Device Architecture (CUDA) interface developed by NVIDIA and carried out on Graphical Processing Units (GPU). GPUs are specifically designated for efficiently processing graphic data sets. However, they are also recently being exploited for achieving excellent computational results for applications non-directly connected with Computer Graphics. The authors show an implementation of SCIARA and present results referred to a Tesla GPU computing processor, a NVIDIA device specifically designed for High Performance Computing, and a Geforce GT 330M commodity graphic card. Their carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size and type of performed memory optimization. Experiments have confirmed the effectiveness and validity of adopting graphics hardware as an alternative to expensive hardware solutions, such as cluster or multi-core machines, for the implementation of Cellular Automata models.

scholarly journals Bioinformatics Performance Comparison of Many-core Tile64 vs. Multi-core Intel Xeon

2014 ◽  
Vol 17 (1) ◽  
Author(s):  
Myriam Kurtz ◽  
Francisco J. Esteban ◽  
Pilar Hernández ◽  
Juan Antonio Caballero ◽  
Antonio Guevara ◽  
...  
Keyword(s):  
Parallel Implementation ◽  
Protein Sequences ◽  
Performance Comparison ◽  
Xeon Phi ◽  
Performance Improvements ◽  
Dna And Rna ◽  
Significant Performance ◽  
The Many ◽  
Many Core ◽  
Intel Xeon

The performance of the many-core Tile64 versus the multi-core Xeon x86 architecture on bioinformatics has been compared. We have used the pairwise algorithm MC64-NW/SW that we have previously developed to align nucleic acid (DNA and RNA) and peptide (protein) sequences for the benchmarking, being an enhanced and parallel implementation of the Needleman-Wunsch and Smith-Waterman algorithms. We have ported the MC64-NW/SW (originally developed for the Tile64 processor), to the x86 architecture (Intel Xeon Quad Core and Intel i7 Quad Core processors) with excellent results. Hence, the evolution of the x86-based architectures towards coprocessors like the Xeon Phi should represent significant performance improvements for bioinformatics.

A GIS-Based Irregular Cellular Automata Model of Land-Use Change

2007 ◽  
Vol 34 (4) ◽  
pp. 708-724 ◽  
Author(s):  
Daniel Stevens ◽  
Suzana Dragićević
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Spatial Data ◽  
Cellular Automata Model ◽  
Population Growth Rates ◽  
Asynchronous Development ◽  
Canadian City ◽  
Ca Model ◽  
Developing Area

This study proposes an alternative cellular automata (CA) model, which relaxes the traditional CA regular square grid and synchronous growth, and is designed for representations of land-use change in rural-urban fringe settings. The model uses high-resolution spatial data in the form of irregularly sized and shaped land parcels, and incorporates synchronous and asynchronous development in order to model more realistically land-use change at the land parcel scale. The model allows urban planners and other stakeholders to evaluate how different subdivision designs will influence development under varying population growth rates and buyer preferences. A model prototype has been developed in a common desktop GIS and applied to a rapidly developing area of a midsized Canadian city.

scholarly journals An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1639
Author(s):  
Seungmin Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Eenjun Hwang
Keyword(s):  
Power Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Input Sequence ◽  
Short Term ◽  
Performance Improvements ◽  
Short Term Load Forecasting ◽  
Significant Performance ◽  
Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

scholarly journals Tensor approach to software implementation of cellular automata model of diffusion

2020 ◽  
Vol 1680 ◽  
pp. 012035
Author(s):  
A K Matolygin ◽  
N A Shalyapina ◽  
M L Gromov ◽  
S N Torgaev
Keyword(s):  
Cellular Automata ◽  
Software Implementation ◽  
Cellular Automata Model
Sign in / Sign up

Export Citation Format

Share Document