1&#x2F;3 time is spent in the p_reduce step, and another 1&#x2F;3 in elemwise.  Not exactly sure what those do, but I&#x27;m guessing it&#x27;s related to the reduce-map-reduce steps of evaluating the standard deviation and then dividing the elements by this value. The mean has to be calculated twice in the formula of the z score.  It sounds like the client-worker communication mechanism might have extra latency.<p>I wonder if this would work if the dask arrays are not equal in length, for example if the files were time series of unequal duration.<p>Also, are there any plans for dask to support distributed numpy functions requiring kernel computation at the array boundaries?  For example scipy.signal.lfilt?  I believe it would require ghosting or further inter-dask-array communication that is not yet present.