A distributed algorithm for multi-tau autocorrelation

Network data-transfer times in distributed simulation environments can be reduced by performing data analysis at the remote source, if the analytical technique does not require the entire set of data at once. This novel multi-tau autocorrelation algorithm allows time-domain data records to be processed in discrete, distributed segments and combined at a later point in time. The new approach agrees with autocorrelation results performed by concatenating the discrete segments before correlation, but it operates with significantly shortened processing times. The multi-tau algorithm also benefits from reduced memory requirements since it does not require access to the entire data record at once, and from improved scalability since the multi-tau algorithm has order O(N), while fast Fourier transform autocorrelation algorithms have order O(N log N). This distributed algorithm has particular utility in simulations of fluorescence correlation spectroscopy or photon correlation spectroscopy.