  Network super point is a kind of special host which plays an important role
in network management and security. For a core network, detecting super points
in real time is a burden task because it requires plenty computing resources to
keep up with the high speed of packets. Previous works try to solve this
problem by using expensive memory, such as static random access memory, and
multi cores of CPU. But the number of cores in CPU is small and each core of
CPU has a high price. In this work, we use a popular parallel computing
platform, graphic processing unit GPU, to mining core network's super point. We
propose a double direction hash functions group which can map hosts randomly
and restore them from a dense structure. Because the high randomness and simple
process of the double direction hash functions, our algorithm reduce the memory
to smaller than one-fourth of other algorithms. Because the small memory
requirement of our algorithm, a low cost GPU, only worth 200 dollars, is fast
enough to deal with a high speed network such as 750 Gb/s. No other algorithm
can cope with such a high bandwidth traffic as accuracy as our algorithm on
such a cheap platform. Experiments on the traffic collecting from a core
network demonstrate the advantage of our efficient algorithm.
