Agent Migration in HPC Systems Using FLAME

Márquez, Claudio; Eduardo, César; Sorribes, Joan

doi:10.1007/978-3-642-54420-0_51

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…They also proposed top‐down replication and bottom‐up replication strategies to investigate computational sharing between simulation instances while maintaining execution accuracy. Márquez et al [10] proposed an agent migration method to solve the workload imbalance problem caused by complex interaction rules. Their method runs based on Flame, and migration routines are automatically generated from predefined templates.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive and optimized agent placement scheme for parallel agent‐based simulation

2021

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This study presents a noble scheme for distributed and parallel simulations with optimized agent placement for simulation instances. The traditional parallel simulation has some limitations in that it does not provide sufficient performance even though using multiple resources. The main reason for this discrepancy is that supporting parallelism inevitably requires additional costs in addition to the base simulation cost. We present a comprehensive study of parallel simulation architectures, execution flows, and characteristics. Then, we identify critical challenges for optimizing large simulations for parallel instances. Based on our cost–benefit analysis, we propose a novel approach to overcome the performance constraints of agent‐based parallel simulations. We also propose a solution for eliminating the synchronizing cost among local instances. Our method ensures balanced performance through optimal deployment of agents to local instances and an adaptive agent placement scheme according to the simulation load. Additionally, our empirical evaluation reveals that the proposed model achieves better performance than conventional methods under several conditions.

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Section: Related Workmentioning

confidence: 99%

“…However, despite the advantages of parallel ABMS, several obstacles make it difficult to achieve the expected performance. The main issue is that special techniques supporting parallel environments inevitably require additional resources in addition to those for a simulation [8–12].…”

Section: Introductionmentioning

confidence: 99%

Adaptive and optimized agent placement scheme for parallel agent‐based simulation

2021

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“…A set of works have proposed a methodology that enables dynamic and automatic performance enhancements for FLAME-generated simulators implementing spatially explicit ABMS [14][15][16]. The methodology introduces a tuning strategy which dynamically minimizes the gaps of the computing and communication workloads between simulation processes.…”

Section: Extended Flamementioning

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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“…First, whenever a TC cycle determines celldivision, the parent-cell sends an agent creation request to the Helper agent. In order to solve this problem, the TC replication process has been redesigned as a two-step operation which is depicted in Figure 7 Different parts of this work have been previously published in [21], [22], and [23] but here we present the general policy that dynamically balances the computational load of the simulation considering also the amount of communication among the compute nodes.…”

Section: Model Performance Optimisationmentioning

confidence: 99%

Colorectal tumour simulation using agent based modelling and high performance computing

Kang

Márquez

Barat

et al. 2017

Future Generation Computer Systems

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450,000 European citizens are diagnosed every year with colorectal cancer (CRC) and more than 230,000 succumb to the disease annually. For this reason, significant resources are dedicated to the identification of more effective therapies for this disease. However, classical assessment techniques for these treatments are slow and costly. Consequently, systems biology researchers at the Royal College of Surgeons in Ireland (RCSI) are developing computational agent-based models simulating tumour growth and treatment responses with the objective of speeding up the therapeutic development process while, at the same time, producing a tool for adapting treatments to patient-specific characteristics. However, the model complexity and the high number of agents to be simulated require a thorough optimisation of the process in order to execute realistic simulations of tumour growth on currently available platforms. We propose to apply the most advanced HPC techniques to achieve the efficient and realistic simulation of a virtual tissue model that mimics tumour growth or regression in space and time. These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied other solid tumours.This work has been partially supported by MICINN-Spain under contract TIN2011- 28689-C02-01 and TIN2014-53234-C2-1-R and GenCat-DIUiE(GRR) 2014-SGR-576. This research was also funded by the European Community’s Framework Programme Seven (FP7) Programme under contract No. 278981\ud 680 AngioPredict and supported by the DJEI/DES/SFI/HEA Irish Centre for High-\ud End Computing (ICHEC).Peer ReviewedPostprint (author's final draft

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Agent Migration in HPC Systems Using FLAME

Cited by 5 publications

References 8 publications

Adaptive and optimized agent placement scheme for parallel agent‐based simulation

Adaptive and optimized agent placement scheme for parallel agent‐based simulation

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

Colorectal tumour simulation using agent based modelling and high performance computing

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