One-to-All Broadcast and All-to-One Reduction

Posted By on March 19, 2016

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Parallel Algorithm Models
All-to-All Broadcast and Reduction

Parallel algorithms often require a single process to send identical data to all other processes or to a subset of them. This operation is known as one-to-all broadcast. Initially, only the source process has the data of size m that needs to be broadcast. At the termination of the procedure, there are p copies of the initial data – one belonging to each process. The dual of one-to-all broadcast is all-to-one reduction. In an all-to-one reduction operation, each of the p participating processes starts with a buffer M containing m words. The data from all processes are combined through an associative operator and accumulated at a single destination process into one buffer of size m. Reduction can be used to find the sum, product, maximum, or minimum of sets of numbers – the i th word of the accumulated M is the sum, product, maximum, or minimum of the ith words of each of the original buffers. Figure 4.1 shows one-to-all broadcast and all-to-one reduction among p processes.


Figure 4.1. One-to-all broadcast and all-to-one reduction.



One-to-all broadcast and all-to-one reduction are used in several important parallel algorithms including matrix-vector multiplication, Gaussian elimination, shortest paths, and vector inner product. In the following subsections, we consider the implementation of one-to-all broadcast in detail on a variety of interconnection topologies.

4.1.1 Ring or Linear Array

A naive way to perform one-to-all broadcast is to sequentially send p – 1 messages from the source to the other p – 1 processes. However, this is inefficient because the source process becomes a bottleneck. Moreover, the communication network is underutilized because only the connection between a single pair of nodes is used at a time. A better broadcast algorithm can be devised using a technique commonly known as recursive doubling. The source process first sends the message to another process. Now both these processes can simultaneously send the message to two other processes that are still waiting for the message. By continuing this procedure until all the processes have received the data, the message can be broadcast in log p steps.

The steps in a one-to-all broadcast on an eight-node linear array or ring are shown in Figure 4.2. The nodes are labeled from 0 to 7. Each message transmission step is shown by a numbered, dotted arrow from the source of the message to its destination. Arrows indicating messages sent during the same time step have the same number.


Figure 4.2. One-to-all broadcast on an eight-node ring. Node 0 is the source of the broadcast. Each message transfer step is shown by a numbered, dotted arrow from the source of the message to its destination. The number on an arrow indicates the time step during which the message is transferred.



Note that on a linear array, the destination node to which the message is sent in each step must be carefully chosen. In Figure 4.2, the message is first sent to the farthest node (4) from the source (0). In the second step, the distance between the sending and receiving nodes is halved, and so on. The message recipients are selected in this manner at each step to avoid congestion on the network. For example, if node 0 sent the message to node 1 in the first step and then nodes 0 and 1 attempted to send messages to nodes 2 and 3, respectively, in the second step, the link between nodes 1 and 2 would be congested as it would be a part of the shortest route for both the messages in the second step.

Reduction on a linear array can be performed by simply reversing the direction and the sequence of communication, as shown in Figure 4.3. In the first step, each odd numbered node sends its buffer to the even numbered node just before itself, where the contents of the two buffers are combined into one. After the first step, there are four buffers left to be reduced on nodes 0, 2, 4, and 6, respectively. In the second step, the contents of the buffers on nodes 0 and 2 are accumulated on node 0 and those on nodes 6 and 4 are accumulated on node 4. Finally, node 4 sends its buffer to node 0, which computes the final result of the reduction.


Figure 4.3. Reduction on an eight-node ring with node 0 as the destination of the reduction.



Example 4.1 Matrix-vector multiplication

Consider the problem of multiplying an n x n matrix A with an n x 1 vector x on an n x n mesh of nodes to yield an n x 1 result vector y. Algorithm 8.1 shows a serial algorithm for this problem. Figure 4.4shows one possible mapping of the matrix and the vectors in which each element of the matrix belongs to a different process, and the vector is distributed among the processes in the topmost row of the mesh and the result vector is generated on the leftmost column of processes.


Figure 4.4. One-to-all broadcast and all-to-one reduction in the multiplication of a 4 x 4 matrix with a 4 x 1 vector.



Since all the rows of the matrix must be multiplied with the vector, each process needs the element of the vector residing in the topmost process of its column. Hence, before computing the matrix-vector product, each column of nodes performs a one-to-all broadcast of the vector elements with the topmost process of the column as the source. This is done by treating each column of the n x n mesh as an n-node linear array, and simultaneously applying the linear array broadcast procedure described previously to all columns.

After the broadcast, each process multiplies its matrix element with the result of the broadcast. Now, each row of processes needs to add its result to generate the corresponding element of the product vector. This is accomplished by performing all-to-one reduction on each row of the process mesh with the first process of each row as the destination of the reduction operation.

For example, P9 will receive x [1] from P1 as a result of the broadcast, will multiply it with A[2, 1] and will participate in an all-to-one reduction with P8, P10, and P11 to accumulate y[2] on P8.

4.1.2 Mesh

We can regard each row and column of a square mesh of p nodes as a linear array of graphics/01icon35.gif nodes. So a number of communication algorithms on the mesh are simple extensions of their linear array counterparts. A linear array communication operation can be performed in two phases on a mesh. In the first phase, the operation is performed along one or all rows by treating the rows as linear arrays. In the second phase, the columns are treated similarly.

Consider the problem of one-to-all broadcast on a two-dimensional square mesh with graphics/01icon35.gif rows and graphics/01icon35.gif columns. First, a one-to-all broadcast is performed from the source to the remaining (graphics/01icon44.gif) nodes of the same row. Once all the nodes in a row of the mesh have acquired the data, they initiate a one-to-all broadcast in their respective columns. At the end of the second phase, every node in the mesh has a copy of the initial message. The communication steps for one-to-all broadcast on a mesh are illustrated in Figure 4.5 for p = 16, with node 0 at the bottom-left corner as the source. Steps 1 and 2 correspond to the first phase, and steps 3 and 4 correspond to the second phase.


Figure 4.5. One-to-all broadcast on a 16-node mesh.



We can use a similar procedure for one-to-all broadcast on a three-dimensional mesh as well. In this case, rows of p1/3 nodes in each of the three dimensions of the mesh would be treated as linear arrays. As in the case of a linear array, reduction can be performed on two- and three-dimensional meshes by simply reversing the direction and the order of messages.

4.1.3 Hypercube

The previous subsection showed that one-to-all broadcast is performed in two phases on a two-dimensional mesh, with the communication taking place along a different dimension in each phase. Similarly, the process is carried out in three phases on a three-dimensional mesh. A hypercube with 2d nodes can be regarded as a d-dimensional mesh with two nodes in each dimension. Hence, the mesh algorithm can be extended to the hypercube, except that the process is now carried out in d steps – one in each dimension.

Figure 4.6 shows a one-to-all broadcast on an eight-node (three-dimensional) hypercube with node 0 as the source. In this figure, communication starts along the highest dimension (that is, the dimension specified by the most significant bit of the binary representation of a node label) and proceeds along successively lower dimensions in subsequent steps. Note that the source and the destination nodes in three communication steps of the algorithm shown in Figure 4.6 are identical to the ones in the broadcast algorithm on a linear array shown in Figure 4.2. However, on a hypercube, the order in which the dimensions are chosen for communication does not affect the outcome of the procedure. Figure 4.6 shows only one such order. Unlike a linear array, the hypercube broadcast would not suffer from congestion if node 0 started out by sending the message to node 1 in the first step, followed by nodes 0 and 1 sending messages to nodes 2 and 3, respectively, and finally nodes 0, 1, 2, and 3 sending messages to nodes 4, 5, 6, and 7, respectively.


Figure 4.6. One-to-all broadcast on a three-dimensional hypercube. The binary representations of node labels are shown in parentheses.



4.1.4 Balanced Binary Tree

The hypercube algorithm for one-to-all broadcast maps naturally onto a balanced binary tree in which each leaf is a processing node and intermediate nodes serve only as switching units. This is illustrated in Figure 4.7for eight nodes. In this figure, the communicating nodes have the same labels as in the hypercube algorithm illustrated in Figure 4.6. Figure 4.7 shows that there is no congestion on any of the communication links at any time. The difference between the communication on a hypercube and the tree shown in Figure 4.7 is that there is a different number of switching nodes along different paths on the tree.


Figure 4.7. One-to-all broadcast on an eight-node tree.



4.1.5 Detailed Algorithms

A careful look at Figures 4.2, 4.5, 4.6, and 4.7 would reveal that the basic communication pattern for one-to-all broadcast is identical on all the four interconnection networks considered in this section. We now describe procedures to implement the broadcast and reduction operations. For the sake of simplicity, the algorithms are described here in the context of a hypercube and assume that the number of communicating processes is a power of 2. However, they apply to any network topology, and can be easily extended to work for any number of processes (Problem 4.1).

Algorithm 4.1 shows a one-to-all broadcast procedure on a 2d-node network when node 0 is the source of the broadcast. The procedure is executed at all the nodes. At any node, the value of my_id is the label of that node. Let X be the message to be broadcast, which initially resides at the source node 0. The procedure performs d communication steps, one along each dimension of a hypothetical hypercube. In Algorithm 4.1, communication proceeds from the highest to the lowest dimension (although the order in which dimensions are chosen does not matter). The loop counter i indicates the current dimension of the hypercube in which communication is taking place. Only the nodes with zero in the i least significant bits of their labels participate in communication along dimension i. For instance, on the three-dimensional hypercube shown in Figure 4.6,i is equal to 2 in the first time step. Therefore, only nodes 0 and 4 communicate, since their two least significant bits are zero. In the next time step, when i = 1, all nodes (that is, 0, 2, 4, and 6) with zero in their least significant bits participate in communication. The procedure terminates after communication has taken place along all dimensions.

The variable mask helps determine which nodes communicate in a particular iteration of the loop. The variable mask has d (= log p) bits, all of which are initially set to one (Line 3). At the beginning of each iteration, the most significant nonzero bit of mask is reset to zero (Line 5). Line 6 determines which nodes communicate in the current iteration of the outer loop. For instance, for the hypercube of Figure 4.6, mask is initially set to 111, and it would be 011 during the iteration corresponding to i = 2 (the i least significant bits of mask are ones). The AND operation on Line 6 selects only those nodes that have zeros in their i least significant bits.

Among the nodes selected for communication along dimension i , the nodes with a zero at bit position i send the data, and the nodes with a one at bit position i receive it. The test to determine the sending and receiving nodes is performed on Line 7. For example, in Figure 4.6, node 0 (000) is the sender and node 4 (100) is the receiver in the iteration corresponding to i = 2. Similarly, for i = 1, nodes 0 (000) and 4 (100) are senders while nodes 2 (010) and 6 (110) are receivers.

Algorithm 4.1 works only if node 0 is the source of the broadcast. For an arbitrary source, we must relabel the nodes of the hypothetical hypercube by XORing the label of each node with the label of the source node before we apply this procedure. A modified one-to-all broadcast procedure that works for any value of source between 0 and p – 1 is shown in Algorithm 4.2. By performing the XOR operation at Line 3, Algorithm 4.2relabels the source node to 0, and relabels the other nodes relative to the source. After this relabeling, the algorithm of Algorithm 4.1 can be applied to perform the broadcast.

Algorithm 4.3 gives a procedure to perform an all-to-one reduction on a hypothetical d-dimensional hypercube such that the final result is accumulated on node 0. Single node-accumulation is the dual of one-to-all broadcast. Therefore, we obtain the communication pattern required to implement reduction by reversing the order and the direction of messages in one-to-all broadcast. Procedure ALL_TO_ONE_REDUCE(d, my_id, m,X, sum) shown in Algorithm 4.3 is very similar to procedure ONE_TO_ALL_BC(d, my_id, X) shown in Algorithm 4.1. One difference is that the communication in all-to-one reduction proceeds from the lowest to the highest dimension. This change is reflected in the way that variables mask and i are manipulated in Algorithm 4.3. The criterion for determining the source and the destination among a pair of communicating nodes is also reversed (Line 7). Apart from these differences, procedure ALL_TO_ONE_REDUCE has extra instructions (Lines 13 and 14) to add the contents of the messages received by a node in each iteration (any associative operation can be used in place of addition).

Algorithm 4.1 One-to-all broadcast of a message X from node 0 of a d-dimensional p-node hypercube (d = log p). AND and XOR are bitwise logical-and and exclusive-or operations, respectively.
1.    procedure ONE_TO_ALL_BC(d, my_id, X) 
2.    begin 
3.       mask := 2d - 1;                  /* Set all d bits of mask to 1 */ 
4.       for i := d - 1 downto 0 do       /* Outer loop */ 
5.           mask := mask XOR 2i;         /* Set bit i of mask to 0 */ 
6.           if (my_id AND mask) = 0 then /* If lower i bits of my_id are 0 */ 
7.               if (my_id AND 2i) = 0 then 
8.                   msg_destination := my_id XOR 2i; 
9.                   send X to msg_destination; 
10.              else 
11.                  msg_source := my_id XOR 2i; 
12.                  receive X from msg_source; 
13.              endelse; 
14.          endif; 
15.      endfor; 
16.   end ONE_TO_ALL_BC
Algorithm 4.2 One-to-all broadcast of a message X initiated by source on a d-dimensional hypothetical hypercube. The AND and XOR operations are bitwise logical operations.
1.   procedure GENERAL_ONE_TO_ALL_BC(d, my_id, source, X) 
2.   begin 
3.      my_virtual id := my_id XOR source; 
4.      mask := 2d - 1; 
5.      for i := d - 1 downto 0 do   /* Outer loop */ 
6.          mask := mask XOR 2i;    /* Set bit i of mask to 0 */ 
7.          if (my_virtual_id AND mask) = 0 then 
8.              if (my_virtual_id AND 2i) = 0 then 
9.                  virtual_dest := my_virtual_id XOR 2i; 
10.                 send X to (virtual_dest XOR source); 
        /* Convert virtual_dest to the label of the physical destination */ 
11.             else 
12.                 virtual_source := my_virtual_id XOR 2i; 
13.                 receive X from (virtual_source XOR source); 
        /* Convert virtual_source to the label of the physical source */ 
14.             endelse; 
15.     endfor; 
Algorithm 4.3 Single-node accumulation on a d-dimensional hypercube. Each node contributes a message X containing m words, and node 0 is the destination of the sum. The AND and XOR operations are bitwise logical operations.
1.   procedure ALL_TO_ONE_REDUCE(d, my_id, m, X, sum) 
2.   begin 
3.      for j := 0 to m - 1 do sum[j] := X[j]; 
4.      mask := 0; 
5.      for i := 0 to d - 1 do 
            /* Select nodes whose lower i bits are 0 */ 
6.          if (my_id AND mask) = 0 then 
7.              if (my_id AND 2i)  0 then 
8.                  msg_destination := my_id XOR 2i; 
9.                  send sum to msg_destination; 
10.             else 
11.                 msg_source := my_id XOR 2i; 
12.                 receive X from msg_source; 
13.                 for j := 0 to m - 1 do 
14.                     sum[j] :=sum[j] + X[j]; 
15.             endelse; 
16.          mask := mask XOR 2i; /* Set bit i of mask to 1 */ 
17.     endfor; 

4.1.6 Cost Analysis

Analyzing the cost of one-to-all broadcast and all-to-one reduction is fairly straightforward. Assume that p processes participate in the operation and the data to be broadcast or reduced contains m words. The broadcast or reduction procedure involves log p point-to-point simple message transfers, each at a time cost of ts + tw m. Therefore, the total time taken by the procedure is

Equation 4.1


Parallel Algorithm Models
All-to-All Broadcast and Reduction

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Posted by Akash Kurup

Founder and C.E.O, World4Engineers Educationist and Entrepreneur by passion. Orator and blogger by hobby