#Reduction

Reduction, which is the process of combining (or reducing) the results of several sub-calculations into a single combined (reduced) result, is very common, and is the second half of the very powerful map-reduce form of parallel programming. In the exercise in the last section you used reduction to form the global sum of the total number of points inside and outside the circle, calculated as the sum of the number of points inside and outside the circle calculated by each process’s iterations of the loop. While it may appear easy to write your own reduction code, using MPI_Send and MPI_Recv, it is actually very hard to write efficient reduction code. This is because reduction requires communication between all processes, where perhaps only one process at a time is allowed to update the sum on the master process. Reduction can actually be implemented much more efficiently, e.g. perhaps by dividing processes into pairs, and getting each pair to sum their results, and then dividing into pairs of pairs, and summing the pairs of pairs results, etc. (this method is known as binary tree reduction - see here for a more in-depth discussion of reduction algorithms).

So reduction is actually quite complicated to implement if you want it to be efficient and work well. Fortunately, you don’t have to implement it, as MPI provides a MPI_Reduce function which has a complete implementation for you! You can use MPI_Reduce by providing the input message, which is sent by each process and contains the set of data to be reduced, the output message, which will be sent to a specified process, and will contain the reduced output, and the operation, which refers to the numerical operation that must be applied to reduce the variables from each process together. The operators include;

• MPI_SUM - adds (sums) all of the variables together
• MPI_PROD - forms the product of all of the variables
• MPI_MAX - finds the maximum value from the variables
• MPI_MIN - finds the minimum value from the variables

A full list can be found here. Note that it is also possible for you to define your own functions that can be used to reduce variables together, via MPI_Op_create, although describing how that works is beyond this introductory course.

To make this clear, the following links provide the code for the fixed loop counting examples from the last page which use MPI_Reduce rather than MPI_Send / MPI_Recv pairs for summing up the number of iterations performed;

Note that MPI_Reduce will send the reduced result back to only the designated process (which should normally be your master process). If you want all of the processes to receive the reduced result, then you should call MPI_Allreduce. [MPI_Allreduce] is equivalent to [MPI_Reduce], except that it sends the reduced result to every process in the MPI team.

##Exercise

Edit your program to estimate pi so that it uses reduction to form the sum of the number of points inside and outside the circle.

Here’s the answers for checking (no peeking before you’ve finished!)

Compare with OpenMP

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