OpenACC Acceleration of an Agent-Based Biological Simulation Framework

Matt, Stack,; Macklin, Paul; Schleicher, Robert; Chandrasekaran, Sunita

doi:10.48550/arxiv.2110.13368

Cited by 2 publications

(2 citation statements)

References 27 publications

(38 reference statements)

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“…This adds up to other computational costs of running PhysiBoSS simulations on a regular laptop. The 2D simulations can take a couple of minutes, but 3D simulations require at least 1 h. One way to tackle this issue that is currently being investigated is to parallelize further this simulation by using GPUs ( Stack et al 2021 ) or multiple MPI nodes ( Saxena et al 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Multiscale model of the different modes of cancer cell invasion

et al. 2023

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Motivation Mathematical models of biological processes altered in cancer are built using the knowledge of complex networks of signaling pathways, detailing the molecular regulations inside different cell types, such as tumor cells, immune and other stromal cells. If these models mainly focus on intracellular information, they often omit a description of the spatial organization among cells and their interactions, and with the tumoral microenvironment. Methods We present here a model of tumor cell invasion simulated with PhysiBoSS, a multiscale framework which combines agent-based modeling and continuous time Markov processes applied on Boolean network models. With this model, we aim to study the different modes of cell migration and to predict means to block it by considering not only spatial information obtained from the agent-based simulation but also intracellular regulation obtained from the Boolean model. Results Our multiscale model integrates the impact of gene mutations with the perturbation of the environmental conditions and allows the visualization of the results with 2D and 3D representations. The model successfully reproduces single and collective migration processes and is validated on published experiments on cell invasion. In silico experiments are suggested to search for possible targets that can block the more invasive tumoral phenotypes. Supplementary information https://github.com/sysbio-curie/Invasion_model_PhysiBoSS

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Section: Discussionmentioning

confidence: 99%

Multiscale model of the different modes of cancer cell invasion

et al. 2023

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“…Related work has focused on high performance implementations of agent based simulation tools within distributed environments, 2,5 or alternatively has sought to accelerate simulations within particular domains or for specific models. [27][28][29] There have been other recent works which focus on software engineering challenges related to agent based simulation tools, in particular making software accessible in high level languages such as Python. 30,31 This article is unique in attempting to address the software engineering challenge of providing a general purpose simulator capable of utilising GPU acceleration.…”

Section: Discussionmentioning

confidence: 99%

FLAME GPU 2: A framework for flexible and performant agent based simulation on GPUs

et al. 2023

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Agent based modelling (ABM) offers a powerful abstraction for scientific study in a broad range of domains. The use of agent based simulators encourages good software engineering design such as separation of concerns, that is, the uncoupling of the model description from its implementation detail. A major limitation in current approaches to ABM simulation is that of the trade off between simulator flexibility and performance. It is common that highly optimised simulations, such as those which target graphics processing units (GPU) hardware, are implemented as standalone software. This work presents a software framework (FLAME GPU 2) which balances flexibility with performance for general purpose ABM. Methods for ensuring high computational efficacy are demonstrated by, minimising data movement, and ensuring high device utilisation by exploiting opportunities for concurrent code execution within a model and through the use of ensembles of simulations. A novel hierarchical sub‐modelling approach is also presented which can be used to model certain types of recursive behaviours. This feature is shown to be essential in providing a mechanism to resolve competition for resources between agents within a parallel environment which would otherwise introduce race conditions. To understand the performance characteristics of the software, a benchmark model with millions of agents is used to explore the use of simulation ensembles and to parametrically investigate concurrent code execution within a model. Performance speedups are demonstrated of 3.5prefix×$$ \times $$ and 10prefix×$$ \times $$ respectively over a baseline GPU implementation. Our hierarchical sub‐modelling approach is used to demonstrate the implementation of a recursive algorithm to resolve competition of agent movement which occurs as a result of agent desire to simultaneously occupy discrete areas high in a ‘resource’. The algorithm is used to implement a classical socio‐economics model, Sugarscape, with populations of up to 16M agents.

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OpenACC Acceleration of an Agent-Based Biological Simulation Framework

Cited by 2 publications

References 27 publications

Multiscale model of the different modes of cancer cell invasion

Multiscale model of the different modes of cancer cell invasion

FLAME GPU 2: A framework for flexible and performant agent based simulation on GPUs

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