When calculating correlation networks from multi-electrode array (MEA) data, one works with extensive computations. Unfortunately, as the MEAs grow bigger, the time needed for the computation grows even more: calculating pair-wise correlations for current 60 channel systems can take hours on normal commodity computers whereas for future 1000 channel systems it would take almost 280 times as long, given that the number of pairs increases with the square of the number of channels. Even taking into account the increase of speed in processors, soon it can be unfeasible to compute correlations in a single computer. Parallel computing is a way to sustain reasonable calculation times in the future. We provide a general tool for rapid computation of correlation networks which was tested for: (a) a single computer cluster with 16 cores, (b) the Newcastle Condor System utilizing idle processors of university computers and (c) the inter-cluster, with 192 cores. Our reusable tool provides a simple interface for neuroscientists, automating data partition and job submission, and also allowing coding in any programming language. It is also sufficiently flexible to be used in other high-performance computing environments.
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