Cellular ANTomata as Engines for Highly Parallel Pattern Processing

Abstract

One important approach to high-performance computing has a (relatively) simple physical computer architecture emulate virtual algorithmic architectures (VAAs) that are highly optimized for important application domains. We expose the Cellular ANTomaton (CAnt) computing model—cellular automata enhanced with mobile FSMs (Ants)—as a highly efficient VAA for a variety of pattern-processing problems that are inspired by biocomputing applications. We illustrate the CAnt model via a scalable design for an $n \times n$ CAnt that solves the following bio-inspired problem in linear time.

• The Pattern-Assembly Problem.

• Inputs: a length-n master pattern $\varPi$ and r test patterns $\pi _0, \ldots , \pi _{r-1}$, of respective lengths $m_0 \ge \cdots \ge m_{r-1}$.

• The problem: Find every sequence $\langle \pi _{j_0}, \ldots , \pi _{j_{s-1}} \rangle$ of $\pi _k$’s, possibly with repetitions, that “assemble” (i.e., concatenate) to produce $\varPi$; i.e., $\pi _{j_0} \cdots \pi _{j_{s-1}} = \varPi$.

• Timing: $m_1 + \cdots + m_r + O(n)$ steps, with a quite-small big-O constant.

This research was supported in part by US NSF Grant CSR-1217981.