  A novel computing model, called \emph{Probe Machine}, is proposed in this
paper. Different from Turing Machine, Probe Machine is a fully-parallel
computing model in the sense that it can simultaneously process multiple pairs
of data, rather than sequentially process every pair of linearly-adjacent data.
In this paper, we establish the mathematical model of Probe Machine as a
9-tuple consisting of data library, probe library, data controller, probe
controller, probe operation, computing platform, detector, true solution
storage, and residue collector. We analyze the computation capability of the
Probe Machine model, and in particular we show that Turing Machine is a special
case of Probe Machine. We revisit two NP-complete problems---i.e., the Graph
Coloring and Hamilton Cycle problems, and devise two algorithms on basis of the
established Probe Machine model, which can enumerate all solutions to each of
these problems by only one probe operation. Furthermore, we show that Probe
Machine can be implemented by leveraging the nano-DNA probe technologies. The
computational power of an electronic computer based on Turing Machine is known
far more than that of the human brain. A question naturally arises: will a
future computer based on Probe Machine outperform the human brain in more ways
beyond the computational power?
