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An efficient algorithm based on weak synchronization for distributed in virtuo biological experiments

机译：一种基于弱同步的虚拟生物实验分布式高效算法

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Virtual Reality is becoming increasingly necessary to study complex systems such as biological systems. Thanks to Virtual Reality, the user is placed at the heart of biological simulations and can carry out experiments as if he were under the same experimental conditions as in vivo or in vitro. We usually call this kind of experiments in virtuo experiments.In order to rapidly develop Virtual Reality applications related to biology, we have already proposed the RelSCOP meta model which makes it possible to easily design biological simulations and undertake in virtuo experiments. This meta model allows to describe a biological system as a composition of its sub-systems and the interactions between the constituents of these sub-systems.Unfortunately, when using a single computer, the number of simulated entities is far from what is needed in biological simulations. It seemed thus necessary to extend the RelSCOP meta model so that it allows distributed computing on a grid. We made this choice because the structure of the RelSCOP meta model is well adapted to a distribution on a grid where the sub-systems which compose a system can be dispatched on different nodes, the synchronization and the coherence of the system being ensured by a Peer-to-Peer architecture.Unlike traditional approaches which propose a spatial distribution, the method we describe in this paper is based on an "organizational" distribution linked to the RelSCOP meta model. This "organizational" distribution is mainly ensured by using two efficient algorithms based on a dead reckoning method, one for a data consistency between nodes and one for a weak synchronization of the nodes involved. These two algorithms are integrated into the behaviors of agents (DIVAs) which are located on each node of the grid. These agents are able to communicate by using a Peer-to-Peer architecture upon the grid.In order to validate our approach, we implement three distributed simulations with increasing complexities and we compare the results with the results obtained in the non-distributed simulations. We get very similar results for the distributed and the non-distributed simulations.
机译：研究诸如生物系统之类的复杂系统，虚拟现实变得越来越必要。由于有了虚拟现实技术，用户可以置于生物学模拟的中心，并且可以像在体内或体外一样的实验条件下进行实验。我们通常在虚拟实验中称这种实验。为了快速开发与生物学相关的虚拟现实应用，我们已经提出了RelSCOP元模型，该模型使得可以轻松地设计生物学模拟并在虚拟实验中进行。这个元模型允许将生物系统描述为其子系统的组成以及这些子系统的组成部分之间的相互作用。不幸的是，当使用单台计算机时，模拟实体的数量远远超出了生物学中所需的数量模拟。因此，似乎有必要扩展RelSCOP元模型，以便允许在网格上进行分布式计算。我们之所以做出此选择，是因为RelSCOP元模型的结构非常适合于网格上的分布，在网格上可以将组成系统的子系统分派到不同的节点上，并通过Peer确保系统的同步性和一致性。对等体系结构。与提出空间分布的传统方法不同，我们在本文中描述的方法基于链接到RelSCOP元模型的“组织”分布。这种“组织”分布主要是通过使用两种基于航位推算方法的有效算法来确保的，一种用于节点之间的数据一致性，另一种用于所涉及的节点的弱同步。这两个算法已集成到位于网格每个节点上的代理（DIVA）的行为中。这些代理能够通过在网格上使用对等体系结构进行通信。为了验证我们的方法，我们实施了三个分布式仿真，并且复杂性不断提高，并将结果与​​非分布式仿真中获得的结果进行了比较。对于分布式和非分布式仿真，我们得到非常相似的结果。

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