Profile-based spatial partitioning for parallel simulation of large-scale wildfires

Guo, Song; Hu, Xiaolin

doi:10.1016/j.simpat.2011.07.003

Cited by 3 publications

(2 citation statements)

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“…[3] Ultimately, it will realize the blueprint of above and promote the rapid and sustainable development of school. This campus will be a new form of university informationization.…”

Section: A What Is Smart Campusmentioning

confidence: 99%

A Study on Smart Campus Model in the Era of Big Data

Xiong¹

2017

Proceedings of the 2016 2nd International Conference on Economics, Management Engineering and Education Technology (ICEMEET 201

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Abstract. With the rapid development of science and technology like big data, Internet of Things and cloud computing, the construction of Smart Campus is going to be the trend of universities. This paper proceeds from the analysis of the challenges that university informationization is faced with. The author put forward the concept of Smart Campus under the big data, analyzed the typical characteristics of college Smart Campus, then designed the structure of Smart Campus with the combination of physical space and digital space, and finally discussed the construction of the Smart Campus.

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“…[3] Ultimately, it will realize the blueprint of above and promote the rapid and sustainable development of school. This campus will be a new form of university informationization.…”

Section: A What Is Smart Campusmentioning

confidence: 99%

A Study on Smart Campus Model in the Era of Big Data

Xiong¹

2017

Proceedings of the 2016 2nd International Conference on Economics, Management Engineering and Education Technology (ICEMEET 201

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“…Guo and Hu created an algorithm to partition a simulation model of a wildfire using profiling data [13].…”

Section: Chapter 3 Related Workmentioning

confidence: 99%

Profile driven partitioning of parallel simulation models

Alt¹,

Wilsey²

2014

Proceedings of the Winter Simulation Conference 2014

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A considerable amount of research into effective parallelization for discrete event driven simulation has been conducted over the past few decades. However, most of this research has targeted the parallel simulation infrastructure; focusing on data structures, algorithms, and synchronization methods for the parallel and distributed simulation kernels. While this focus has successfully improved and refined the performance of parallel discrete event simulation kernels, little effort has been directed toward analyzing and preparing the simulation model itself for parallel execution. Model specific optimizations could have significant performance implications, but have been largely ignored. This fact is complicated by the lack of a widely used simulation and modeling language for many domains. The lack of a common language is, however, not entirely insurmountable. For example, the partitioning and assignment of objects from the simulation model onto the hardware platform is generally performed by the simulation infrastructure. While partitioning can have dramatic impacts on the communication frequencies between the concurrently executed objects, most existing parallel simulation infrastructures do little to address this opportunity. This thesis addresses the partitioning and assignment of objects within a simulation model for parallel execution. The specific target of this effort is to develop a partitioning and assignment strategy for use in the WARPED parallel simulation kernel that has been developed and maintained at the University of Cincinnati. The focus of the work is to develop a general purpose solution that can function for any simulation model that has been prepared for execution on the WARPED kernel. The specific solution exploits a sequential kernel from the WARPED project to pre-simulate the simulation model to obtain profile data regarding the frequency of events communicated between objects. This event frequency data is then used to develop partitions to minimize the amount of event exchanges between the objects in the different partitions. This partition information is then used during the initialization sequences of the WARPED kernel to assign each partition i to a unique processing node in the parallel cluster. This method is independent of the simulation model and compute platform. Experimental results with existing simulation models from the WARPED project show that this method can achieve up to a six-fold improvement in run time over the naive partitioning algorithm that was previously used by the WARPED kernel.

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A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters

Kwon

Yun

Kim

2022

Sensors

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As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.

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Profile-based spatial partitioning for parallel simulation of large-scale wildfires

Cited by 3 publications

References 20 publications

A Study on Smart Campus Model in the Era of Big Data

A Study on Smart Campus Model in the Era of Big Data

Profile driven partitioning of parallel simulation models

A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters

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