Next: 3.10 Summary Up: 3 A Quantitative Basis for Design Previous: 3.8 Input/Output

# 3.9 Case Study: Shortest-Path Algorithms

We conclude this chapter by using performance models   to compare four different parallel algorithms for the all-pairs   shortest-path problem. This is an important problem in graph theory and has applications in communications, transportation, and electronics problems. It is interesting because analysis shows that three of the four algorithms can be optimal in different circumstances, depending on tradeoffs between computation and communication costs.

Figure 3.23: A simple directed graph, G, and its adjacency matrix, A.

The all-pairs shortest-path problem involves finding the shortest path between all pairs of vertices in a graph. A graph G=(V,E) comprises a set V of N vertices, , and a set E V of edges connecting vertices in V . In a directed graph, each edge also has a direction, so edges and , , are distinct. A graph can be represented as an adjacency matrix A in which each element (i,j) represents the edge between element i and j . if there is an edge ; otherwise, =0 (Figure 3.23).

A path from vertex to vertex is a sequence of edges , , ..., from E in which no vertex appears more than once. For example, , is a path from vertex 1 to vertex 0 in Figure 3.23. The shortest path between two vertices and in a graph is the   path that has the fewest edges. The single-source shortest-path problem requires that we find the shortest path from a single vertex to all other vertices in a graph. The all-pairs shortest-path problem requires that we find the shortest path between all pairs of vertices in a graph. We consider the latter problem and present four different parallel algorithms, two based on a sequential shortest-path algorithm due to Floyd and two based on a sequential algorithm due to Dijkstra. All four algorithms take as input an N N adjacency matrix A and compute an N N matrix S , with the length of the shortest path from to , or a distinguished value () if there is no path.

## 3.9.1 Floyd's Algorithm

Floyd's all-pairs shortest-path algorithm is given as   Algorithm 3.1. It derives the matrix S in   N steps, constructing at each step k an intermediate matrix I(k) containing the best-known shortest distance between each pair of nodes. Initially, each is set to the length of the edge , if the edge exists, and to otherwise. The k th step of the algorithm considers each in turn and determines whether the best-known path from to is longer than the combined lengths of the best-known paths from to and from to . If so, the entry is updated to reflect the shorter path (Figure 3.24). This comparison operation is performed a total of times; hence, we can approximate the sequential cost of this algorithm as , where is the cost of a single comparison operation.

Figure 3.24: The fundamental operation in Floyd's sequential shortest-path algorithm: Determine whether a path going from to via is shorter than the best-known path from to .

#### Parallel Floyd 1.

The first parallel Floyd algorithm is based on a one-dimensional, rowwise domain decomposition of the intermediate matrix I and the output matrix S . Notice that this means the algorithm can use at most N processors. Each task has one or more adjacent rows of I and is responsible for performing computation on those rows. That is, it executes the following logic.

```
for  k = 0 to  N-1

for  i= local_i_start to local_i_end

for  j = 0 to  N-1

(k+1) = min((k), (k)+(k))

endfor

endfor

endfor

```

Figure 3.25: Parallel version of Floyd's algorithm based on a one-dimensional decomposition of the I matrix. In (a), the data allocated to a single task are shaded: a contiguous block of rows. In (b), the data required by this task in the k th step of the algorithm are shaded: its own block and the k th row.

In the k th step, each task requires, in addition to its local data, the values , , ..., , that is, the k th row of I (Figure 3.25). Hence, we specify that the task with this row broadcast it to all other tasks. This communication can be performed by using a tree structure in steps. Because there are N such broadcasts and each message has size N , the cost is

Notice that each task must serve as the ``root'' for at least one broadcast (assuming ). Rather than defining P binary tree structures, it suffices to connect the P tasks using a hypercube structure (Chapter 11), which has the useful property of allowing any node to broadcast to all other nodes in steps.

#### Parallel Floyd 2.

An alternative parallel version of Floyd's algorithm uses a two-dimensional decomposition of the various matrices. This version allows the use of up to processors and requires that each task execute the following logic.

```
for  k = 0 to  N-1
for  i= local_i_start to local_i_end
for  j= local_j_start to local_j_end

(k+1) = min((k), (k)+(k))

endfor
endfor
endfor

```

Figure 3.26: Parallel version of Floyd's algorithm based on a two-dimensional decomposition of the I matrix. In (a), the data allocated to a single task are shaded: a contiguous submatrix. In (b), the data required by this task in the k th step of the algorithm are shaded: its own block, and part of the k th row and column.

In each step, each task requires, in addition to its local data, values from two tasks located in the same row and column of the 2-D task array (Figure 3.26). Hence, communication requirements at the k th step can be structured as two broadcast operations: from the task in each row that possesses part of column k to all other tasks in that row, and from the task in each column that possesses part of row k to all other tasks in that column.

In each of N steps, values must be broadcast to the tasks in each row and column, and the total cost is

Notice that each task must serve as the ``root'' node for at least one broadcast to each task in the same row and column of the 2-D task array. These communication requirements can be satisfied by connecting tasks in the same row or column in a hypercube structure.

## 3.9.2 Dijkstra's Algorithm

Dijkstra's single-source shortest-path algorithm computes   all shortest paths from a single vertex, . It can also be used   for the all-pairs shortest-path problem, by the simple expedient of applying it N times---once to each vertex , ..., .

Dijkstra's sequential single-source algorithm is given as Algorithm 3.2. It maintains as T the set of vertices for which shortest paths have not been found, and as the shortest known path from to vertex . Initially, T=V and all . At each step of the algorithm, the vertex in T with the smallest d value is removed from T . Each neighbor of in T is examined to see whether a path through would be shorter than the currently best-known path (Figure 3.27).

Figure 3.27: The comparison operation performed in Dijkstra's single-source shortest-path algorithm. The best-known path from the source vertex to vertex is compared with the path that leads from to and then to .

An all-pairs algorithm executes Algorithm 3.2 N times, once for each vertex. This involves comparisons and takes time F , where is the cost of a single comparison in Floyd's algorithm and F is a constant. Empirical studies show that F 1.6; that is, Dijkstra's algorithm is slightly more expensive than Floyd's algorithm.

#### Parallel Dijkstra 1.

The first parallel Dijkstra algorithm replicates the graph in each of P tasks. Each task executes the sequential algorithm for N/P vertices. This algorithm requires no communication but can utilize at most N processors. Because the sequential Dijkstra algorithm is F times slower than the sequential Floyd algorithm, the parallel algorithm's execution time is

#### Parallel Dijkstra 2.

The second parallel Dijkstra algorithm allows for the case when P>N . We define N sets of P/N tasks. Each set of tasks is given the entire graph and is responsible for computing shortest paths for a single vertex (Figure 3.28). Within each set of tasks, the vertices of the graph are partitioned. Hence, the operation Find with minimum

requires first a local computation to find the local vertex with minimum d and second a reduction involving all P/N tasks in the same set in order to determine the globally minimum . The reduction can be achieved by using the butterfly communication structure of Section 2.4.1, in steps. Hence, as the reduction is performed N times and involves two values, the total cost of this algorithm is

Figure 3.28: The second parallel Dijkstra algorithm allocates P/N tasks to each of N instantiations of Dijkstra's single-source shortest-path algorithm. In this figure, N=9 and P=36 , and one set of P/N=4 tasks is shaded.

## 3.9.3 Shortest-Path Algorithms Summary

Table 3.7 summarizes the performance models developed for   the four all-pairs shortest-path algorithms. Clearly, Floyd 2 will always be more efficient that Floyd 1. Both algorithms have the same computation costs and send the same number of messages, but Floyd 2 communicates considerably less data. On the other hand, Floyd 1 is easier to implement. Algorithms Dijkstra 1 and 2 will be more efficient than Floyd 2 in certain circumstances. For example, Dijkstra 1 is more efficient than Floyd 2 if P N and

Table 3.7: Performance of four parallel shortest-path algorithms.

In addition to these factors, we must consider the fact that algorithms Dijkstra 1 and Dijkstra 2 replicate the graph P and P/N times, respectively. This replication may compromise the scalability of these algorithms. Also, the cost of replicating an originally distributed graph must be considered if (as is likely) the shortest-path algorithm forms part of a larger program in which the graph is represented as a distributed data structure.

Clearly, the choice of shortest-path algorithm for a particular problem will involve complex tradeoffs between flexibility, scalability, performance, and implementation complexity. The performance models developed in this case study provide a basis for   evaluating these tradeoffs.

Next: 3.10 Summary Up: 3 A Quantitative Basis for Design Previous: 3.8 Input/Output