Data Parallelism - Java 8 Lambdas - page 101

It's quite complex, so I'll go through how this works in a few steps. The input parameter n is
the size of the time window we're calculating our moving average over. At we take a copy
of our input data. Because our prefix calculation is a mutating operation, we do this to avoid
altering the original source.
In we apply the prefix operation, adding up values in the process. So now our sums vari-
able holds the running total of the sums so far. For example, given the input 0, 1, 2, 3,
4, 3.5 , it would hold 0.0, 1.0, 3.0, 6.0, 10.0, 13.5 .
Now that we have the complete running totals, we can find the sum over the time window by
subtracting the running total at the beginning of the time window. The average is this divided
by n . We can do this calculation using the existing streams library, so let's use it! We kick off
the stream in by using Intstream.range to get a stream ranging over the indices of the
values we want.
At we subtract away the running total at the start and then do the division in order to get
the average. It's worth noting that there's an edge case for the running total at element n - 1,
where there is no running total to subtract to begin with. Finally, at , we convert the Stream
back to an array.
Key Points
▪ Data parallelism is a way to split up work to be done on many cores at the same time.
▪ If we use the streams framework to write our code, we can utilize data parallelism by
calling the parallel or parallelStream methods.
▪ The five main factors influencing performance are the data size, the source data struc-
ture, whether the values are packed, the number of available cores, and how much pro-
cessing time is spent on each element.
Exercises
1. The code in Example 6-10 sequentially sums the squares of numbers in a Stream .
Make it run in parallel using streams.
Example 6-10. Sequentially summing the squares of numbers in a list