OpenMP parallelization of the SCIARA Cellular Automata lava flow model: performance analysis on shared-memory computers

Oliverio, Marco; Spataro, William; D'Ambrosio, Donato; Rongo, Rocco; Spingola, Giuseppe; Trunfio, Giuseppe A.

doi:10.1016/j.procs.2011.04.029

Cited by 23 publications

(7 citation statements)

References 15 publications

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“…Then, in Algorithm 1, the bit vector x , representing the current solution of dimension num_f , is computed from the context vector b , and the inactivity counter of each feature included among the current search space is updated by considering the corresponding value in the bit vector x (lines [25][26][27][28][29][30][31][32][33].…”

Section: The Proposed Frameworkmentioning

confidence: 99%

“…The parallel implementation was developed in C++ and OpenMP [28]. The program was compiled with Intel Compiler 2023 and run in three workstations: (i) The first one equipped with an Intel i9-7940X 14-Core CPU (3.10GHz), (ii) the second equipped with a single Intel i7-8750 H 6-Core CPU (2.20GHz), and (iii) the third one with two Intel Xeon X5660 (2.80 GHz) 6-Core CPUs (12 cores in total).…”

Section: Parallel Efficiencymentioning

confidence: 99%

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Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

2023

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In many fields, it is a common practice to collect large amounts of data characterized by a high number of features. These datasets are at the core of modern applications of supervised machine learning, where the goal is to create an automatic classifier for newly presented data. However, it is well known that the presence of irrelevant features in a dataset can make the learning phase harder and, most importantly, can lead to suboptimal classifiers. Consequently, it is becoming increasingly important to be able to select the right subset of features. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features because of the poor scalability of optimization algorithms. In this article, we address the problem using a cooperative coevolutionary approach based on differential evolution. In the proposed algorithm, parallelized for execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adjusting the population size during the optimization results in significant performance improvements. A numerical investigation on some high-dimensional and medium-dimensional datasets shows that, in most cases, the proposed approach can achieve higher classification performance than other state-of-the-art methods.

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Section: The Proposed Frameworkmentioning

confidence: 99%

Section: Parallel Efficiencymentioning

confidence: 99%

Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

2023

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“…Among them, Dattilo et al [12] proposed a distributed CA model to simulate landslide which achieved a good performance on cluster such as Beowulf clusters. Oliverio et al [13] proposed another approach which just uses shared memory based on OpenMP compilation directives in CA models to simulate lava flows. To simulate large scale domains, Bandman [14] proposed an approach that uses shared memory.…”

Section: Related Workmentioning

confidence: 99%

A Parallel Cellular Automaton Applied to Traffic Simulation With Different Driving Style

Zamitha¹,

Zamitha²,

Leal-Toledo

et al. 2017

JCIS

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The traffic movement and the demand by public or private transport are problems of present days. All cities in the world suffer with traffic jammed, air and noise pollution, affecting our life quality. With this in mind, simulation systems have arose as solution to understand traffic phenomenas and help us to design vehicular traffic efficiently. These simulation systems are based on mathematic models so that they can describe the traffic behavior. Among mathematic models employed, Cellular Automaton (CA) are able to mimic the basic characteristics of vehicular traffic. CA models, that take into account different driving style, demand more computation between two consecutive simulation time steps and also require more neighbors to calculate the correct velocity of each vehicle, which implies more memory access. This work proposes a new parallel CA model which includes new policies of acceleration and distance perception based on continuous probability function to describe the unpredictable behavior of driving styles. This model is presented in parallel platform approach in order to evaluate its performance in execution of the proposed model in simulating traffic big cities. The model herein illustrates qualitative and quantitative results similar to those suggested by the literature. Besides, we conducted tests in order to evaluate the parallel execute of the proposed model.

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“…Debris flows create hazard risks, including human injury and death, property losses, social instability, and environmental destruction, and hinder the sustainable development of the economy in mountainous areas [4]. For example, the debris flow disasters that occurred in Zhouqu County on 7 August 2010, in Wenchuan County on 11 July 2013, and in Jiuzhaigou County on 25 July 2016, all resulted in damaged houses, traffic disruptions, a large number of casualties, and huge economic losses. Therefore, it is important to implement a rapid risk analysis of debris flow disasters in residential areas that can provide accurate disaster information.…”

Section: Introductionmentioning

confidence: 99%

A Virtual Geographic Environment for Debris Flow Risk Analysis in Residential Areas

Yin

Zhu

et al. 2017

IJGI

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Emergency risk assessment of debris flows in residential areas is of great significance for disaster prevention and reduction, but the assessment has disadvantages, such as a low numerical simulation efficiency and poor capabilities of risk assessment and geographic knowledge sharing. Thus, this paper focuses on the construction of a VGE (virtual geographic environment) system that provides an efficient tool to support the rapid risk analysis of debris flow disasters. The numerical simulation, risk analysis, and 3D (three-dimensional) dynamic visualization of debris flow disasters were tightly integrated into the VGE system. Key technologies, including quantitative risk assessment, multiscale parallel optimization, and visual representation of disaster information, were discussed in detail. The Qipan gully in Wenchuan County, Sichuan Province, China, was selected as the case area, and a prototype system was developed. According to the multiscale parallel optimization experiments, a suitable scale was chosen for the numerical simulation of debris flow disasters. The computational efficiency of one simulation step was 5 ms (milliseconds), and the rendering efficiency was approximately 40 fps (frames per second). Information about the risk area, risk population, and risk roads under different conditions can be quickly obtained. The experimental results show that our approach can support real-time interactive analyses and can be used to share and publish geographic knowledge.

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OpenMP parallelization of the SCIARA Cellular Automata lava flow model: performance analysis on shared-memory computers

Cited by 23 publications

References 15 publications

Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

A Parallel Cellular Automaton Applied to Traffic Simulation With Different Driving Style

A Virtual Geographic Environment for Debris Flow Risk Analysis in Residential Areas

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