GPU-Based Extended Cellular Model Implementation

Abstract

A Cellular Automaton (Cellular Model) is a convenient and efficient way to solve a broad class of problems that belong to different areas of science, including (but not limited to) physics, math, chemistry, biology. A lot of natural phenomena and common tasks that are described via differential equations are easily modeled this way. But in spite of obvious advantages, CA-based algorithm implementations for common CPUs are highly parallel and hence extremely resource-intensive. Thus, the idea of SIMD GPU-based CA implementation looks feasible. In this paper, we will discuss the problems we faced while developing GPU-based cellular model implementation, and the ways we solved them.

Dynamic routing protocols for wireless touch networks: a review

“…This can significantly limit the applicability of such a structure to nodes that vary considerably in self-organizing networks. The multilayer tree structure seeks to eliminate two major drawbacks of an overlapping network with a single-tree structure [22]. First, in single-tree structures, the few closest nodes are loaded much more heavily than the rest of the network, since the "leaves" of the tree have not been involved in content transmission.…”