FLAME GPU: Complex System Simulation Framework

2017 ◽  
Author(s):  
Paul Richmond ◽  
Mozhgan K. Chimeh
Keyword(s):  
Complex System ◽  
System Simulation ◽  
Simulation Framework

A Fuzzy Cognitive Maps Modeling, Learning and Simulation Framework for Studying Complex System

2011 ◽  
pp. 243-256 ◽  
Author(s):  
Maikel León ◽  
Gonzalo Nápoles ◽  
Ciro Rodriguez ◽  
María M. García ◽  
Rafael Bello ◽  
...  
Keyword(s):  
Complex System ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Simulation Framework

A System Simulation Framework for Modeling Space Modulation Techniques

2020 ◽  
Vol 14 (1) ◽  
pp. 1435-1446 ◽  
Author(s):  
Omar Hiari ◽  
Raed Mesleh ◽  
Abdullah Al-Khatib
Keyword(s):  
System Simulation ◽  
Simulation Framework ◽  
Modulation Techniques ◽  
Space Modulation

ON DESIGN OF A COUPLING COMPONENT FOR PARALLEL MULTIMODELING

2011 ◽  
Vol 02 (04) ◽  
pp. 445-458 ◽  
Author(s):  
DALI WANG ◽  
MICHAEL HARMON ◽  
MICHAEL BERRY ◽  
LOUIS GROSS
Keyword(s):  
Natural Resource Management ◽  
Dynamic Load ◽  
System Simulation ◽  
Data Communication ◽  
Ecosystem Restoration ◽  
Dynamic Load Balancing ◽  
Multiscale Simulations ◽  
Level System ◽  
Simulation Framework ◽  
On Demand

A component-based simulation framework is a favorable choice for parallel multiscale simulations, which require a dedicated coupling component to support flexible data communication/exchange, computational parallelism, and on-demand dynamic load balancing. In this paper, a coupling component (the Coupler) was designed to support integrated parallel multimodeling. An integrated ecological multimodeling, part of the across trophic level system simulation (ATLSS) for Everglades ecosystem restoration, has been used to demonstrate the advantage of the Coupler for natural resource management applications.

scholarly journals A Probabilistic Prediction Approach for Memory Resource of Complex System Simulation in Cloud Computing Environment

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1826
Author(s):  
Shuai Wang ◽  
Yiping Yao ◽  
Feng Zhu ◽  
Wenjie Tang ◽  
Yuhao Xiao
Keyword(s):  
Complex System ◽  
Cloud Computing ◽  
System Simulation ◽  
Probabilistic Prediction ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Ensemble Approach ◽  
Memory Resource ◽  
Optimistic Synchronization ◽  
Prediction Approach

Accurate memory resource prediction can achieve optimal performance for complex system simulation (CSS) using optimistic parallel execution in the cloud computing environment. However, because of the varying memory resource demands of CSS applications caused by the simulation entity scale and frequent optimistic synchronization, the existing approaches are unable to predict the memory resource required by a CSS application accurately, which cannot take full advantage of the elasticity and symmetry of cloud computing. In this paper, a probabilistic prediction approach based on ensemble learning, which regards the entity scale and frequent optimistic synchronization as the important features, is proposed. The approach using stacking strategy consists of a two-layer architecture. The first-layer architecture includes two kinds of base models, namely, back-propagation neural network (BPNN) and random forest (RF). The root mean squared error-based pruning algorithm is designed to choose the optimal subset of the base models. The second-layer is the Gaussian process regression (GPR) model, which is applied to quantify the uncertainty information in the probabilistic prediction for memory resources. A series of experiments are presented to prove that the proposed approach can achieve higher accuracy and performance compared to RF, BPNN, GPR, Bagging ensemble approach, and Regressive Ensemble Approach for Prediction.

scholarly journals Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 891
Author(s):  
Xiong ◽  
Zhu ◽  
Yao ◽  
Tang ◽  
Xiao
Keyword(s):  
Complex System ◽  
System Simulation ◽  
Simulation Models ◽  
Selection Method ◽  
Semantic Search ◽  
Distributed Scheduling ◽  
Parallel Operation ◽  
Optimal Service ◽  
Service Oriented ◽  
Search And Selection

With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences.

Application of MetaSynthesis in Complex System Simulation

2015 ◽  
Vol 713-715 ◽  
pp. 1631-1634
Author(s):  
Ling Jie Kong ◽  
Jin Li ◽  
Xin Jun Zhao ◽  
Fang Zhang
Keyword(s):  
Complex System ◽  
System Simulation ◽  
Key Technology ◽  
Simulation Based ◽  
Complexity Problem ◽  
Study Methodology ◽  
Equipment System

Metasynthesis is the dialectical unity of reductionism and holism, which is the study methodology of complex giant system and complexity problem. Based on joint demonstrating of equipment system as an example, this paper studies the metasynthesis in the application of complex system simulation. Based on analyzing key technology about hall for workshop of metasynthetic engineering of joint demonstrating,the HWME(Hall for Workshop of Metasynthetic Engineering) of joint demonstrating of equipment system is established.

Sign in / Sign up

Export Citation Format

Share Document