# All-Reduce and Prefix-Sum Operations

Posted By on March 19, 2016

The communication pattern of all-to-all broadcast can be used to perform some other operations as well. One of these operations is a third variation of reduction, in which each node starts with a buffer of size m and the final results of the operation are identical buffers of size m on each node that are formed by combining the original p buffers using an associative operator. Semantically, this operation, often referred to as the all-reduce operation, is identical to performing an all-to-one reduction followed by a one-to-all broadcast of the result. This operation is different from all-to-all reduction, in which p simultaneous all-to-one reductions take place, each with a different destination for the result.

An all-reduce operation with a single-word message on each node is often used to implement barrier synchronization on a message-passing computer. The semantics of the reduction operation are such that, while executing a parallel program, no node can finish the reduction before each node has contributed a value.

Equation 4.5

Algorithm 4.7 can be used to perform a sum of p numbers if my_msg, msg, and result are numbers (rather than messages), and the union operation (‘‘) on Line 8 is replaced by addition.

Finding prefix sums (also known as the scan operation) is another important problem that can be solved by using a communication pattern similar to that used in all-to-all broadcast and all-reduce operations. Given pnumbers n0, n1, …, np-1 (one on each node), the problem is to compute the sums for all k between 0 and p – 1. For example, if the original sequence of numbers is <3, 1, 4, 0, 2>, then the sequence of prefix sums is <3, 4, 8, 8, 10>. Initially, nk resides on the node labeled k, and at the end of the procedure, the same node holds sk . Instead of starting with a single numbers, each node could start with a buffer or vector of size m and the m-word result would be the sum of the corresponding elements of buffers.

Figure 4.13 illustrates the prefix sums procedure for an eight-node hypercube. This figure is a modification of Figure 4.11. The modification is required to accommodate the fact that in prefix sums the node with label kuses information from only the k-node subset of those nodes whose labels are less than or equal to k. To accumulate the correct prefix sum, every node maintains an additional result buffer. This buffer is denoted by square brackets in Figure 4.13. At the end of a communication step, the content of an incoming message is added to the result buffer only if the message comes from a node with a smaller label than that of the recipient node. The contents of the outgoing message (denoted by parentheses in the figure) are updated with every incoming message, just as in the case of the all-reduce operation. For instance, after the first communication step, nodes 0, 2, and 4 do not add the data received from nodes 1, 3, and 5 to their result buffers. However, the contents of the outgoing messages for the next step are updated.

##### Figure 4.13. Computing prefix sums on an eight-node hypercube. At each node, square brackets show the local prefix sum accumulated in the result buffer and parentheses enclose the contents of the outgoing message buffer for the next step.

Since not all of the messages received by a node contribute to its final result, some of the messages it receives may be redundant. We have omitted these steps of the standard all-to-all broadcast communication pattern from Figure 4.13, although the presence or absence of these messages does not affect the results of the algorithm. Algorithm 4.9 gives a procedure to solve the prefix sums problem on a d-dimensional hypercube.

##### Algorithm 4.9 Prefix sums on a d-dimensional hypercube.
```1.   procedure PREFIX_SUMS_HCUBE(my_id, my number, d, result)
2.   begin
3.      result := my_number;
4.      msg := result;
5.      for i := 0 to d - 1 do
6.          partner := my_id XOR 2i;
7.          send msg to partner;