  Computational neuroscience is being revolutionized with the advent of
multi-electrode arrays that provide real-time, dynamic, perspectives into brain
function. Mining event streams from these chips is critical to understanding
the firing patterns of neurons and to gaining insight into the underlying
cellular activity. We present a GPGPU solution to mining spike trains. We focus
on mining frequent episodes which captures coordinated events across time even
in the presence of intervening background/"junk" events. Our algorithmic
contributions are two-fold: MapConcatenate, a new computation-to-core mapping
scheme, and a two-pass elimination approach to quickly find supported episodes
from a large number of candidates. Together, they help realize a real-time
"chip-on-chip" solution to neuroscience data mining, where one chip (the
multi-electrode array) supplies the spike train data and another (the GPGPU)
mines it at a scale unachievable previously. Evaluation on both synthetic and
real datasets demonstrate the potential of our approach.

Distributed, Parallel, and Cluster Computing;Databases