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

Moreno, Amparo; Rodrı́guez, Juan J.; Beltrán, Daniel; Sikora, Anna; Jorba, Josep; Eduardo, César

doi:10.1007/s11227-018-2688-8

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…We improve upon these works by providing an in-depth analysis of each performance-relevant component. Efforts in the direction of a standard agent-based benchmark have been made by Moreno et al [43] and Rousset et al [57]. However, these synthetic benchmarks fall short of representing a realistic range of agent-based simulations by over-simplifying memory access patterns and assuming that agents always move randomly.…”

Section: Related Workmentioning

confidence: 99%

High-Performance and Scalable Agent-Based Simulation with BioDynaMo

Breitwieser

Hesam

Rademakers

et al. 2023

Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

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Agent-based modeling plays an essential role in gaining insights into biology, sociology, economics, and other fields. However, many existing agent-based simulation platforms are not suitable for large-scale studies due to the low performance of the underlying simulation engines. To overcome this limitation, we present a novel high-performance simulation engine.We identify three key challenges for which we present the following solutions. First, to maximize parallelization, we present an optimized grid to search for neighbors and parallelize the merging of thread-local results. Second, we reduce the memory access latency with a NUMA-aware agent iterator, agent sorting with a space-filling curve, and a custom heap memory allocator. Third, we present a mechanism to omit the collision force calculation under certain conditions.Our evaluation shows an order of magnitude improvement over Biocellion, three orders of magnitude speedup over Cortex3D and NetLogo, and the ability to simulate 1.72 billion agents on a single server.Supplementary Materials, including instructions to reproduce the results, are available at: https://doi.org/10.5281/ zenodo.6463816 CCS Concepts: • Computing methodologies → Massively parallel and high-performance simulations; Agent / discrete models; Parallel algorithms; • Software and its engineering → Software performance.

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Section: Related Workmentioning

confidence: 99%

High-Performance and Scalable Agent-Based Simulation with BioDynaMo

Breitwieser

Hesam

Rademakers

et al. 2023

Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

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“…Repast HPC [18] was designed to enhance the scalability of Repast Simphony. Experiments have shown that Repast HPC outperforms other general-purpose distributed simulation frameworks by adopting optimizations such as point-to-point communication and load balancing [18,32,34,48]. However, Repast HPC faces several scalability bottlenecks.…”

Section: Related Workmentioning

confidence: 99%

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Tian,

Lindner,

Nissl

et al. 2023

Proc. ACM Manag. Data

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We generalize the bulk-synchronous parallel (BSP) processing model to make it better support agent-based simulations. Such simulations frequently exhibit hierarchical structure in their communication patterns which can be exploited to improve performance. We allow for the creation of temporary artificial network partitions during which agents synchronize only locally within their group in a way that does not compromise the correctness of a simulation. We have built a distributed engine, CloudCity, which uses this idea to improve the locality of computation, communication, and synchronization in such simulations. We experimentally evaluate the performance of our system on a benchmark of simulation workloads and compare it against other popular BSP-like systems, obtaining insights into the impact of various system design choices and optimization on simulation engine performance.

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“…If we look at Lengnick (2013), for example, we see that the incorporated features include households, firms and banks that behave and interact according to simple rules. Moreno et al (2019) does not consider particular models but rather looks at setting up a benchmark for various agent-based model software platforms. It looks at which features are typically deemed essential for an agent-based model, such as the ability to incorporate interaction rules and to simulate large numbers of agents.…”

Section: Introductionmentioning

confidence: 99%

“…By implementing these features in various agent-based modelling platforms, the performance is assessed by looking at the computational performance (such as execution time) of the platforms. An interesting avenue for future work would be to extend the approach of Moreno et al (2019) from agent-based modelling platforms to agent-based models.…”

Section: Introductionmentioning

confidence: 99%

Towards a Validation Methodology for Macroeconomic Agent-Based Models

Tieleman

2021

Comput Econ

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Agent-based models provide a promising new tool in macroeconomic research. Questions have been raised, however, regarding the validity of such models. A methodology of macroeconomic agent-based model (MABM) validation, that provides a deeper understanding of validation practices, is required. This paper takes steps towards such a methodology by connecting three elements. First, is a foundation of model validation in general. Second is a classification of models dependent on how the model is validated. An important distinction in this classification is the difference between mechanism and target validation. Third, is a framework that revolves around the relationship between the structure of models of complex systems with emergent properties and validation in practice. Important in this framework is to consider MABMs as modelling multiple non-trivial levels. Connecting these three elements provides us with a methodology of the validation of MABMs and allows us to come to the following conclusions regarding MABM validation. First, in MABMs, mechanisms at a lower level are distinct from, but provide input to higher levels of mechanisms. Since mechanisms at different levels are validated in different ways we can come to a specific characterization of MABMs within the model classification framework. Second, because the mechanisms of MABMs are validated in a direct way at the level of the agent, MABMs can be seen as a move towards a more realist approach to modelling compared to DSGE.

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

Cited by 6 publications

References 17 publications

High-Performance and Scalable Agent-Based Simulation with BioDynaMo

High-Performance and Scalable Agent-Based Simulation with BioDynaMo

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Towards a Validation Methodology for Macroeconomic Agent-Based Models

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