We note that a recently described data structure, the Neighborhood Grid, is equivalent to a data structure developed in the mid-1980s called the Monotonic Lagrangian Grid (MLG). The MLG was originally developed to support high-performance molecular and fluid dynamics simulations on both supercomputer and vector processing architectures and still finds use in those and other areas. In this paper we emphasize that the rediscovery of the MLG offers benefits to users of the Neighborhood Grid in the form of an existing literature with results relevant to its efficient implementation in various contexts while users of the MLG similarly benefit from new theoretical results obtained for the Neighborhood grid.

我们注意到，最近描述的数据结构“邻域网格”等效于1980年代中期开发的称为单调拉格朗日网格（MLG）的数据结构。MLG最初是为了在超级计算机和矢量处理体系结构上支持高性能的分子和流体动力学仿真而开发的，并且仍然可以在这些领域和其他领域使用。在本文中，我们着重指出，MLG的重新发现以现有文献的形式为邻域网格的用户带来了好处，其结果与在各种情况下的有效实施相关，而MLG的用户同样受益于为该网格获得的新理论结果。邻域网格。