Care HPS: A high performance simulation tool for parallel and distributed agent-based modeling

Borges, Francisco; Gutierrez-Milla, Albert; Luque, Emilio; Suppi, Remo

doi:10.1016/j.future.2016.08.015

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…Toolkits such as D-MASON (Cordasco et al, 2014) initially attempted to avoid the complexity of MPI (at least in the Java environment) in distributing and coordinating agents across processes in favor of sockets and the Java Message Service before turning to MPI as a more performant solution for interprocess communication. Other ABM toolkits such as Pandora (Rubio-Campillo 2014), Care HPS (Borges et al, 2017), and Repast HPC (Collier and North 2013) also use MPI as the message-passing layer, while Flame GPU (Richmond and Chimeh 2017) distributes agents across a GPU such that their behavior can be executed in parallel. Other areas of emphasis are strategies for load balancing and the optimal partitioning of agents using different topologies and ghosting techniques (Borges et al, 2017;Collier et al, 2015).…”

Section: Agent-based Modelingmentioning

confidence: 99%

“…Other ABM toolkits such as Pandora (Rubio-Campillo 2014), Care HPS (Borges et al, 2017), and Repast HPC (Collier and North 2013) also use MPI as the message-passing layer, while Flame GPU (Richmond and Chimeh 2017) distributes agents across a GPU such that their behavior can be executed in parallel. Other areas of emphasis are strategies for load balancing and the optimal partitioning of agents using different topologies and ghosting techniques (Borges et al, 2017;Collier et al, 2015).…”

Section: Agent-based Modelingmentioning

confidence: 99%

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A population data-driven workflow for COVID-19 modeling and learning

Ozik

Wozniak

Collier

et al. 2021

The International Journal of High Performance Computing Applica

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CityCOVID is a detailed agent-based model that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces, and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of protective behaviors of individuals on viral transmissibility. Throughout the COVID-19 epidemic, CityCOVID model outputs have been provided to city, county, and state stakeholders in response to evolving decision-making priorities, while incorporating emerging information on SARS-CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible, and performant analytical platform for planning and crisis response.

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Section: Agent-based Modelingmentioning

confidence: 99%

Section: Agent-based Modelingmentioning

confidence: 99%

A population data-driven workflow for COVID-19 modeling and learning

Ozik

Wozniak

Collier

et al. 2021

The International Journal of High Performance Computing Applica

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“…In this line of research, the Care HPS (i.e. from high performance simulation) tool [8] supports agent-based modeling with parallel computing for increasing the performance of the simulations.…”

Section: Related Workmentioning

confidence: 99%

A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems

2021

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The upcoming avenue of IoT with the massive generated data makes it really hard to train centralized systems with machine learning in real-time. This problem can be addressed with learn-based edge computing systems where the learning is performed in a distributed way on the nodes. In particular, this work focuses on developing multi-agent systems for implementing learn-based edge computing systems. The diversity of methodologies in agent-oriented software engineering reflects the complexity of developing multi-agent systems. The division of the development processes into method fragments facilitates the application of agent-oriented methodologies and their study. In this line of research, this work proposes a database for implementing a repository of method fragments considering the development of learn-based edge computing systems and the information recommended by the FIPA technical committee. This repository makes method fragments available from different methodologies and computerizes certain metrics and queries over the existing method fragments. This work has compared the performance of several combinations of dimensionality reduction methods and machine learning techniques (i.e. support vector regression, k-nearest neighbors and multi-layer perceptron neural networks) in a simulator of a learn-based edge computing system for estimating profits and customers.

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“…Several frameworks for generating parallel ABMS simulators have been developed taking advantage of the common characteristics of these applications. Among them, we can mention FLAME [3], FLAME GPU [4], Repast HPC [5], EcoLab [6], D-MASON [7], Pandora [8] or Care-HPS [9]. They present differences in the way agents and contexts are specified (e.g., C, C++, Java, XML, Tcl), in the way communications are managed (e.g., explicit messages, agent replication), in the presence of a load balancing mechanism, in the performance of the resulting simulator, etc.…”

Section: Introductionmentioning

confidence: 99%

Designing a benchmark for the performance evaluation of agent-based simulation applications on HPC

et al. 2018

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Agent-based modeling and simulation (ABMS) is a class of computational models for simulating the actions and interactions of autonomous agents with the goal of assessing their effects on a system as a whole. Several frameworks for generating parallel ABMS applications have been developed taking advantage of their common characteristics, but there is a lack of a general benchmark for comparing the performance of the generated applications. We propose and design a benchmark that takes into consideration the most common characteristics of this type of applications and includes parameters for influencing their relevant performance aspects. We provide an initial implementation of the benchmark for FLAME, FLAME GPU, Repast HPC and EcoLab, some of the most popular parallel ABMS platforms, and use it for comparing the applications generated by these platforms. The obtained results are mostly in agreement with previous studies, but the designed and implemented specification has allowed for testing a wider set of aspects, such as the number of interacting agents, the amount of interchanged data or the evolution of the workload and obtaining more reliable results.

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Care HPS: A high performance simulation tool for parallel and distributed agent-based modeling

Cited by 11 publications

References 24 publications

A population data-driven workflow for COVID-19 modeling and learning

A population data-driven workflow for COVID-19 modeling and learning

A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems

Designing a benchmark for the performance evaluation of agent-based simulation applications on HPC

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