Air pollution modelling using a graphics processing unit with CUDA

The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) - a parallel computing architecture - has been developed by NVIDIA to utilize this performance in general purpose computations. Here we show for the first time a possible application of GPU for environmental studies serving as a basement for decision making strategies. A stochastic Lagrangian particle model has been developed on CUDA to estimate the transport and the transformation of the radionuclides from a single point source during an accidental release. Our results show that parallel implementation achieves typical acceleration values in the order of 80-120 times compared to CPU using a single-threaded implementation on a 2.33 GHz desktop computer. Only very small differences have been found between the results obtained from GPU and CPU simulations, which are comparable with the effect of stochastic transport phenomena in atmosphere. The relatively high speedup with no additional costs to maintain this parallel architecture could result in a wide usage of GPU for diversified environmental applications in the near future.Comment: 5 figure

Simulation of reaction-diffusion processes in three dimensions using CUDA

Numerical solution of reaction-diffusion equations in three dimensions is one of the most challenging applied mathematical problems. Since these simulations are very time consuming, any ideas and strategies aiming at the reduction of CPU time are important topics of research. A general and robust idea is the parallelization of source codes/programs. Recently, the technological development of graphics hardware created a possibility to use desktop video cards to solve numerically intensive problems. We present a powerful parallel computing framework to solve reaction-diffusion equations numerically using the Graphics Processing Units (GPUs) with CUDA. Four different reaction-diffusion problems, (i) diffusion of chemically inert compound, (ii) Turing pattern formation, (iii) phase separation in the wake of a moving diffusion front and (iv) air pollution dispersion were solved, and additionally both the Shared method and the Moving Tiles method were tested. Our results show that parallel implementation achieves typical acceleration values in the order of 5-40 times compared to CPU using a single-threaded implementation on a 2.8 GHz desktop computer.Comment: 8 figures, 5 table

The computational efficiency of Monte Carlo breakage of articles using serial and parallel processing : a comparison

This paper presents a GPU-based parallelized and a CPU-based serial Monte-Carlo method for breakage of a particle. We compare the efficiency of the graphic card’s graphics processing unit (GPU) and the general-purpose central processing unit (CPU), in a simulation using Monte Carlo (MC) methods for processing the particle breakage. Three applications are used to compare the computational performance times, clock cycles and speedup factors, to find which platform is faster under which conditions. The architecture of the GPU is becoming increasingly programmable; it represents a potential speedup for many applications compared to the modern CPU. The objective of the paper is to compare the performance of the GPU and Intel Core i7-4790 multicore CPU. The implementation for the CPU was written in the C programming language, and the GPU implemented the kernel using Nvidia’s CUDA (Compute Unified Device Architecture). This paper compares the computational times, clock cycles and the speedup factor for a GPU and a CPU, with various simulation settings such as the number of simulation entries (SEs), for a better understanding of the GPU and CPU computational efficiency. It has been found that the number of SEs directly affects the speedup factor.fi=vertaisarvioitu|en=peerReviewed

Development of a GPGPU accelerated tool to simulate advection-reaction-diffusion phenomena in 2D

Computational models are powerful tools to the study of environmental systems, playing a fundamental role in several fields of research (hydrological sciences, biomathematics, atmospheric sciences, geosciences, among others). Most of these models require high computational capacity, especially when one considers high spatial resolution and the application to large areas. In this context, the exponential increase in computational power brought by General Purpose Graphics Processing Units (GPGPU) has drawn the attention of scientists and engineers to the development of low cost and high performance parallel implementations of environmental models. In this research, we apply GPGPU computing for the development of a model that describes the physical processes of advection, reaction and diffusion. This presentation is held in the form of three self-contained articles. In the first one, we present a GPGPU implementation for the solution of the 2D groundwater flow equation in unconfined aquifers for heterogenous and anisotropic media. We implement a finite difference solution scheme based on the Crank- Nicolson method and show that the GPGPU accelerated solution implemented using CUDA C/C++ (Compute Unified Device Architecture) greatly outperforms the corresponding serial solution implemented in C/C++. The results show that accelerated GPGPU implementation is capable of delivering up to 56 times acceleration in the solution process using an ordinary office computer. In the second article, we study the application of a diffusive-logistic growth (DLG) model to the problem of forest growth and regeneration. The study focuses on vegetation belonging to preservation areas, such as riparian buffer zones. The study was developed in two stages: (i) a methodology based on Artificial Neural Network Ensembles (ANNE) was applied to evaluate the width of riparian buffer required to filter 90% of the residual nitrogen; (ii) the DLG model was calibrated and validated to generate a prognostic of forest regeneration in riparian protection bands considering the minimum widths indicated by the ANNE. The solution was implemented in GPGPU and it was applied to simulate the forest regeneration process for forty years on the riparian protection bands along the Ligeiro river, in Brazil. The results from calibration and validation showed that the DLG model provides fairly accurate results for the modelling of forest regeneration. In the third manuscript, we present a GPGPU implementation of the solution of the advection-reaction-diffusion equation in 2D. The implementation is designed to be general and flexible to allow the modeling of a wide range of processes, including those with heterogeneity and anisotropy. We show that simulations performed in GPGPU allow the use of mesh grids containing more than 20 million points, corresponding to an area of 18,000 km? in a standard Landsat image resolution.Os modelos computacionais s?o ferramentas poderosas para o estudo de sistemas ambientais, desempenhando um papel fundamental em v?rios campos de pesquisa (ci?ncias hidrol?gicas, biomatem?tica, ci?ncias atmosf?ricas, geoci?ncias, entre outros). A maioria desses modelos requer alta capacidade computacional, especialmente quando se considera uma alta resolu??o espacial e a aplica??o em grandes ?reas. Neste contexto, o aumento exponencial do poder computacional trazido pelas Unidades de Processamento de Gr?ficos de Prop?sito Geral (GPGPU) chamou a aten??o de cientistas e engenheiros para o desenvolvimento de implementa??es paralelas de baixo custo e alto desempenho para modelos ambientais. Neste trabalho, aplicamos computa??o em GPGPU para o desenvolvimento de um modelo que descreve os processos f?sicos de advec??o, rea??o e difus?o. Esta disserta??o ? apresentada sob a forma de tr?s artigos. No primeiro, apresentamos uma implementa??o em GPGPU para a solu??o da equa??o de fluxo de ?guas subterr?neas 2D em aqu?feros n?o confinados para meios heterog?neos e anisotr?picos. Foi implementado um esquema de solu??o de diferen?as finitas com base no m?todo Crank- Nicolson e mostramos que a solu??o acelerada GPGPU implementada usando CUDA C / C ++ supera a solu??o serial correspondente implementada em C / C ++. Os resultados mostram que a implementa??o acelerada por GPGPU ? capaz de fornecer acelera??o de at? 56 vezes no processo da solu??o usando um computador de escrit?rio comum. No segundo artigo estudamos a aplica??o de um modelo de crescimento log?stico difusivo (DLG) ao problema de crescimento e regenera??o florestal. O estudo foi desenvolvido em duas etapas: (i) Aplicou-se uma metodologia baseada em Comites de Rede Neural Artificial (ANNE) para avaliar a largura da faixa de prote??o rip?ria necess?ria para filtrar 90% do nitrog?nio residual; (ii) O modelo DLG foi calibrado e validado para gerar um progn?stico de regenera??o florestal em faixas de prote??o rip?rias considerando as larguras m?nimas indicadas pela ANNE. A solu??o foi implementada em GPGPU e aplicada para simular o processo de regenera??o florestal para um per?odo de quarenta anos na faixa de prote??o rip?ria ao longo do rio Ligeiro, no Brasil. Os resultados da calibra??o e valida??o mostraram que o modelo DLG fornece resultados bastante precisos para a modelagem de regenera??o florestal. No terceiro artigo, apresenta-se uma implementa??o em GPGPU para solu??o da equa??o advec??o-rea??o-difus?o em 2D. A implementa??o ? projetada para ser geral e flex?vel para permitir a modelagem de uma ampla gama de processos, incluindo caracter?sticas como heterogeneidade e anisotropia do meio. Neste trabalho mostra-se que as simula??es realizadas em GPGPU permitem o uso de malhas contendo mais de 20 milh?es de pontos (vari?veis), correspondendo a uma ?rea de 18.000 km? em resolu??o de 30m padr?o das imagens Landsat

Rapid-Response Urban CFD Simulations Using a GPU Computing Paradigm on Desktop Supercomputers

In the event of chemical or biological (CB) agent attacks or accidents, first-responders need hazard prediction data to launch effective emergency response action. Accurate and timely knowledge of the wind fields in urban areas is critically important to identify and project the extent of CB agent dispersion to determine the hazard-zone. In their 2008 report (GAO-08-180), U.S. Government Accountability Office has reported that first responders are limited in their ability to detect and model hazardous releases in urban environments. The current set of modeling tools for contaminant dispersion in urban environments rely on empirical assumptions with diagnostic equations (Wang et al. 2003, Williams et al. 2004). The main advantage of these models is their relatively fast turn-around times, although their predictive capabilities can be limited. As part of the Joint Effects Model (JEM), funded by the Department of Defense, urban transport and dispersion models have been evaluated for their rapid-response capabilities. As discussed in Heagy et al. (2007), majority of the urban transport and dispersion models considered in the evaluation study fell short of satisfying the JEM key performance parameter of maximum 10-minutes run-time on a desktop computer, and the models that were able to satisfy the performance parameter were employed at low resolutions

Mintázatképződés, önszerveződés csapadékrendszerekben és a mintázatok tervezése és vezérlése mikro- és nanoskálán = Pattern formation and self-organization in precipitation systems, design and control of patterns in the micro and nanoscales

A projektben a csapadékmintázatok képződését vizsgáltuk. Mintázatképződés változatos térbeli skálán mehet végbe és a mintázatképződés dinamikájának megértésre nagyon fontos, mert ezekkel az ismeretekkel különleges és különféle tulajdonságú anyagot lehet tervezni az anyagtudományokban. Több technikát fejlesztettünk ki a csapadékmintázatok tervezésére és vezérlésére reakció-diffúzió rendszerekben. A kifejlesztett módszerek a következők: (i) a mintázat vezérlése előre programozott elektromos árammal; (ii) a mintázat kontrollja oldhatósági szorzat változtatásával; (iii) a mintázat kontrollja a gél struktúrájának változtatásával; (iv) a mintázat kontrollja mozgó kémiai frontban; a mintázatok vezérlése pH mező segítségével. Ezenfelül, egy új párhuzamosítási platformot fejlesztettünk ki videokártyák használatával CUDA környezetben. Ez az új technika hasznos lehet számos tudományos probléma numerikus megoldásának gyorsításában. | During this project important aspects of the pattern formation in precipitation systems were investigated. Spontaneous pattern formation can be observed at all length scales and much effort has been devoted to gaining insight into the dynamics of theses processes because of design of special materials. We proposed and developed several techniques to control precipitation patterns in reaction-diffusion systems. The developed methods are following: (i) pattern control using predesigned electric currents; (ii) pattern control varying the precipitation threshold (solubility product); (iii) pattern control varying the gel structure; (iv) pattern control in a moving reaction front; (v) pattern control by pH field. Moreover, a new parallel computational framework has also been developed using video card with CUDA for simulation reaction-diffusion processes. This new approach can be useful to accelerate numerical simulations of the scientific problems

libcloudph++ 0.2: single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++

This paper introduces a library of algorithms for representing cloud microphysics in numerical models. The library is written in C++, hence the name libcloudph++. In the current release, the library covers three warm-rain schemes: the single- and double-moment bulk schemes, and the particle-based scheme with Monte-Carlo coalescence. The three schemes are intended for modelling frameworks of different dimensionality and complexity ranging from parcel models to multi-dimensional cloud-resolving (e.g. large-eddy) simulations. A two-dimensional prescribed-flow framework is used in example simulations presented in the paper with the aim of highlighting the library features. The libcloudph++ and all its mandatory dependencies are free and open-source software. The Boost.units library is used for zero-overhead dimensional analysis of the code at compile time. The particle-based scheme is implemented using the Thrust library that allows to leverage the power of graphics processing units (GPU), retaining the possibility to compile the unchanged code for execution on single or multiple standard processors (CPUs). The paper includes complete description of the programming interface (API) of the library and a performance analysis including comparison of GPU and CPU setups.Comment: The library description has been updated to the new library API (i.e. v0.1 -> v0.2 update). The key difference is that the model state variables are now mixing ratios as opposed to densities. The particle-based scheme was supplemented with the "particle recycling" process. Numerous editorial corrections were mad