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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)
(I-SMAC 2017)
Parallelization of Shortest Path Algorithm Using
OpenMP and MPI
Rajashri Awari
Dept.of Info.Tech. MTech.
Yeshwantrao Chavan College of Engineering
Nagpur, India
rajashreewawari@gmail.com
Abstract—Graph problem solve by using the standard graph
Algorithm. Example isLarge matrix problems, graphs solving
problems, equations etc.There is a representation of two
algorithms.The algorithm to find all pair shortest path is Floyd
War shall algorithm, single source shortest path is
Dijkstra’salgorithm. These two algorithm implement in serial
formulation.Parallel algorithm is considerably effective for large
graph size. By using these algorithm find the shortest distance,
such as city to city, routing, networking, social media. Parallel
algorithm used for calculating or finding shortest path of graph.
With the help of graph algorithm these operations can be done in
parallel and reduce the computation time and efficiency. All pair
shortest path problem apply for directed and undirected graph
whose finite nodes and edges in un-weighted and undirected
graph.
Keywords- Shortest Path Algorithms, OpenMP, MPI.
1. INTRODUCTION
All pair shortest path problem find the shortest path in graph
using the graph algorithm. Finding the shortest distance for all
objects in a graph is a common task in solving many day to
day and scientific problems. The algorithm for finding shortest
path, find their application in many fields such as Google
maps, routing protocol etc. There are two types for finding the
shortest path, single source shortest path and all pair shortest
path, using two algorithms Dijkstra’s algorithm and Floyd
algorithm. Dijkstra’s algorithm requires non-negative edge
weights, whereas Floyd's algorithm requires negative-weight
edges. The shortest path algorithm is the basic algorithm for
research hotspot. To improve the transportation system the
best use of shortest path to implement internet protocol
network. It’s use as in the parallel algorithm. Otherwise in
serial implementation it is very difficult improve its
performance. ( Dijkstra’s algorithm). [2]
In serial implementation algorithm it takes long time to find
the shortest distance if all pairs of vertices in graph. So it is
difficult task to find the shortest distance from source to
destination. Therefore use the parallel algorithm or parallel
processing takes less time than the serial implementation. And
It’s easy to calculate the shortest distance or path find in graph
or many other applications.
Fundamental problem in graph theory is single source shortest
path. [1] The All pair shortest path problem and single source
shortest path problem start initially consider source s to all
other vertices in Graph. These two algorithm Floyd and
Dijkstra’s algorithm is most important find the shortest path
problems between all pair of vertices in the graph. The graph
solving problems increase in size, efficient parallel shortest
path processing becomes important as computational and
memory requirements increase. Because of number of size
input required for graph solving problem.[N*N] matrix
problem.
Retrieve information about the process use molecular dynamic
simulation in order to atomistic system that cannot be accessed
during experiment. [3] For large structure or system it takes
long time to perform their operations and this is why the
parallelization used to perform operation in less time.
Performance for the operation in less time to reduce the
efficiency and speedup factor using the OpenMP(Open
multiprocessing) and the MPI (Message Passing Interface)and
also use the parallel Floyd algorithm, parallel Dijkstr’s
algorithm.[3]
The All-Pairs Shortest Paths (APSP) problem and single
source shortest path problem is one of the most important, and
most studied, algorithmic graph problems. Graph shows a
weighted directed graph G = (V,E) where V is the vertices and
E is the edges of the graph.[5] The real-world applications
including VLSI routing, bioinformatics, social network
analysis, and communications. Certain a directed weighted
graph, APSP is the problem of finding the shortest distance
between every two nodes in the graph. This problem reduces
to multiple breadth-first search traversals in the case of an unweighted directed graph. The following figure shows directed
and undirected graph.
Figure (1) shows undirected graph, Figure (2) shows directed
graph as follows.
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(I-SMAC 2017)
path with shortest distance. Thus, running the breadth-firstsearch algorithm (BFS) from every node in the graph is an allpair shortest path algorithm that works on un-weighted graphs.
Minimum Spanning Tree algorithms classified on one of two
approaches, that of Floyd Algorithm, Dijkstra’s Algorithm.
However, many algorithms developed to finding the shortest
path problem. Processor consists at each node presented at the
algorithm in graph.[11] The distance query can be done
through the single source shortest path when x=s, where s is
single source node. To find the shortest distance its require
time to calculate the shortest distance, and total time require
for the process of all edge deletion. The running time of the
algorithm is n number of nodes in the graph and m is the
number of edges. Constructing the graph problem by using the
breath-first search tree from all nodes presented in the tree of
all pair shortest path on un-weighted, undirected graph. This
can be done the time complexity in the order of (m,n).i.e.
o(m,n).
Figure (1) Undirected Graph
Figure (2) Directed Graph
The above two figure shows the Directed and Undirected
Graph. Which shows that Direction from one path to another
or shows the distance between two path.
2. LITERATURE SURVEY
Nicolae Goga[3] for large systems, the simulations can take a
lot of time and this is why MPI parallelization is used in order
to process data in less time. The molecular dynamics package,
GROMACS, accelerates the computation [3] and passes
messages through the communication channels of the MPI. In
this article, a new algorithm for stochastic and dissipative
element dynamics is presented and implement in GROMACS
to be run using the parallelization given by MPI.
Yulian Yu, Wenhong Wei, Qiang Peng [4] the parallel
problem solving parallel speed-up ratio is a measure of the
corresponding benefits. Considering the single source shortest
path problem it’s good for parallel speedup ratio,
computational efficiency to reduce the computation time.
Using vector matrix multiplication operation single source
multilevel graph may find the multiplication operations are
replace by addition operation. And the addition operation
replace by the minimum operation with the help of
parallelization by reducing the time and cost.
Osama G. Attia [5] All pair shortest path difficulty in unweighted directed graphs reduces the problem for finding the
Danny Z. Chen, Every pair of vertices in the graph solving by
the all pair shortest path algorithm. The large graph whose
edges and node is finite number solve by the sequential
manner. It finds the time complexity as well as space
complexity. The time complexity takes O(N3) and space
complexity takes O(N2 ) [6].An interval circular arc graph
solving the sequential algorithm takes O(n1ogn) time for
single source shortest path problem. The all pair shortest paths
information for solving the graph with minimum spanning tree
can be completely available very efficiency.The all pair
shortest path finds the for every nodes or vertices takes o(n 2
logn) time.
Rithesh G. Prabhu and Anjjan S Narayan, proposes a new
method that the all pair shortest path on a GPU used to
increasing the efficiency. The graph consists as node
connected to each other. There are two types of graph, such as
dense graph and sparse graph. Large number of edges consists
of dense graph and the few or small edge consists of the sparse
graph. The dense graph is fully suited for Floyd algorithm and
required the large memory to store the graph or matrix of the
graph. For large graph matrix solving problem can
implemented on GPU, because memory restraints. [8] By
reducing the calculation sparse graph improves their
efficiency. GPU is used as the non-graphic application.
.
3. OVERVIEW OF ALGORITHM
Consider the directed graph G with the n number of vertices.
The vertices considered as 1 to N. And E is the edges of the
graph. So graph can be considered as G = (N, E) where N is
the number of vertices in the graph. Floyd War shall algorithm
consists as the dynamic programming algorithm, which
calculates the shortest path or distance of matrix D k is the Kth
matrix. And this is defined as the Dk(i,j). Where i, j represents
the source and the destination. The shortest path from vertex i
to the vertex j, means the shortest distance between the i to j.
Consider i is the source and j is the destination, which is
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marked as 1 to k, where k is the iteration number. Dijkstra’s
algorithm is calculated the singlesource shortest path problem.
Floyd algorithm solves the problem as directed graph or
undirected graph having weight[16]. Floyd algorithm consider
the one condition that is D(i, j) > D(i, k)+ D(k,j). This
condition must satisfy the Floyd algorithm to calculate the
shortest distance between the all pair of vertices in the graph.
[16]
The Floyd Warshall algorithm compare all possible paths
through the graph between each pair of vertices.[16]
3.1.1 EXAMPLE OF FLOYD ALGORITHM
3.1 FLOYD ALGORITHM
The Floyd-Warshall algorithm use a dynamic programming
approach to solve the all-pairs shortest-paths problem on a
directed graph G = (V,E). The bellow equation shows weight
in between ‘i’ and ‘j’. Where ‘i’ is the source and ‘j’ denote
destination.[21]
Figure (4) Weighted Graph
Let d(k)ij be the weight of a shortest path from vertex. i, j
represents as path from source to destination. Consider I is the
source and j is the destination. wij represents the weight from i
to j shown by the following equation.
A recursivedefinition from the above formulation is given by:
The above matrix shows the adjacency matrix of graph. (fig.4)
Floyd's algorithm has a worst casewhere n is the number of
vertices in the graph. Following steps shows how Floyd
algorithm works as the shortest path using (fig 3) the flow of
algorithm.
The Floyd Algorithm flow is shown below serially such as,
Steps of Floyd Algorithm
The above steps show Floyd algorithm calculate minimum
distance to all pair shortest path by using (fig.4) weighted
graph.
Figure(3). Floyd Algorithm
3.2 DIJKSTRA’S ALGORITHM
The Floyd War shall algorithm is to find the shortest path
between the pair of vertices. Above seven steps (fig 3) shows
the flow of Floyd algorithm. Ans in a weighted graph with
positive or negative edge weights (but with non-negative
cycles) single implementation of the algorithm will find the
lengths (summed weights) of the shortest paths
among all pairs of vertices, though it does not return detail of
the paths themselves. D[i,j] represents the distance of i to j.
Dijkstra's algorithm is used to find the single source shortest
path problem. Consider the single vertex to all other vertices
in the graph. Dijkstra’s algorithm requires the non-negative
weight edge cycle. .Applying the Problem on Graph to find
the distance between source to destination. For a weighted
graph G = (V, E, w), the single-source shortest weighted path
sproblem is to find the nearest paths from a vertex v is
subsetof V to every other vertices in V, where V is the finite
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set of vertices, E is the finite set of edge, w is weight between
the nodes of graph. Dijkstra's algorithm solves the single
source shortest-path problem on directed and undirected
graphs with non-negative weights edgs. Dijkstra's algorithm,
find the minimum path from single vertex. Dijkstra’s
algorithm is similar to Prim's minimum spanning tree
algorithm. Resembling Prim's algorithm, it incrementally find
the shortest paths from s to the other vertices of graph (G). It
is also greedy. i.e., it always selects an edge to a vertex that
appeared closest.[13]
The algorithm works as follows:
1. Assign to every node a tentative distance value: set it to
zero for our initial node and to infinity for all other nodes.
2. Mark all nodes unvisited. Set the initial node as current.
Create a set of the unvisited nodes called the unvisited
set consisting of all the nodes except the initial node.
3. For the current node, consider all of its unvisited neighbors
and calculate the tentative distances. For example, if the
current node A is marked with a distance of 6, and the edge
connecting it with a neighbor B has length 2, then the distance
to B (through A) will be 6+2=8. If this distance isless than the
previously recorded tentative distance of B, then overwrite
that distance. Even though a neighbor has been examined, it is
not marked as “visited” at this time, and it remains in
the unvisited set.
4. When complete the neighbors of the current node and mark
the current node as visited and remove it from the unvisited
set. A visited node will never be checked again.
5. If the destination node has been marked visited (when
planning a route between two specific nodes) or if the smallest
tentative distance among the nodes in the unvisited set is
infinity (when planning a complete traversal), then stop. The
algorithm has finished.
6.Select the unvisited node that is marked with the smallest
tentative distance, and set it as the new “current node” then go
back to step 3.
Dijkstra’s algorithm works the single vertex to all other
vertex.It consists of the single source. But it can also be used
to solve the all-pairs shortest paths problem by executing the
single-source algorithm on each process, for eachvertex v.
This algorithm as Dijkstra's all-pairs shortest paths algorithm,
The complexity of the all-pairs algorithm is Q(n3).Dijkstra's
shortest path algorithm is implement and existing, and the
performances of parallel and serial execution are compared.
The parallel time execution is better than the serial time
execution. Because its take minimum time for the execution.
The algorithm implementation was parallelized using
OpenMP (Open Multi-Processing).[13]
3.2.1 EXAMPLE OF DIJKSTRA’S ALGORITHM
Steps of Dijkstra’s Algorithm
Above example shows the steps of Dijkstra’s algorithm using
the flow of algorithm. (fig.5)
4. TECHNIQUES
Figure (5) Dijkstra’s Algorithm
The two algorithms used in this paper as Floyd Warshall and
Dijkstra’s Algorithm. These Algorithms implemented in c,
c++ for serial formulation. For large graph matrix problem
solve these two algorithms as serially. By using dataset as
large matrix got from search engine which consist node to
node and its weight. Large matrix size consists as 500*500,
1000*1000, 2000*2000. Using large size matrix converted
algorithm in serial form implanted in c, c++. And the serial
form can be converted into the parallel form using OpenMP
and MPI for reducing or consuming time and the efficiency.
Requirement of software is c,c++. With the help of matrix size
up to 2000 implemented serial form by using automatic
program which consist or enter the size of matrix manually.
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(I-SMAC 2017)
And another program implanted as write matrix, it consist as
enter the matrix for example 4*4 matrix etc. Create the file
name as input which runs the main program as Floyd and
Dijkstras algorithm.
Following shows the
serial
implementation of Floyd and Dijkstra’s algorithm.
Where N shows the input size of matrix and number of node,
Ts is the serial time execution of the algorithm in second. It
will be shows that the time execution of Dijkstra’s algorithm
is smaller than the Floyd algorithm, Because Floyd algorithm
solved as all pair shortest path where Dijkstra’s algorithm
single source shortest path.
Further implementation or work to be carried of these two
algorithms is parallel implantation of these two algorithms.
5. STEPS
1.
2.
3.
Serial Implementation of Floyd and Dijkstra’s
algorithm.
Parallel Implementation of Floyd and Dijkstra’s
Algorithm.
Comparison of serial and parallel.
6. PARALLEL PROGRAMMING ENVIRONMENT
Parallel programming environment consist OpenMP. OpenMP
stands for Open Multi-Processing. Following shows the
theoretical idea of OpenMP and MPI.
OpenMP is a paradigm and suitable application programming
interface (API) for writing multiple threads programs on a
shared memory computer.[17] There are three components of
application programming interface such as compiler directive,
runtime library routines and environment variables. OpenMP
consist of the Fork-Join model. Fork-Join is the execution
model for parallel programming execution. OpenMP
programming performs operation on the single thread as the
initial thread. The threads are executed as the sequentially or
serial form, otherwise parallel construct create. Fig shows the
fork-join model. Master thread is called as join. Derive thread
and master thread is works simultaneously. At the starting of
the program only one thread is active this thread is execute
serially.[17]
Ahead finishing point of the parallel section, those copied
threads will stop or hang up, and only the master thread
continue, which is called a join. The OpenMP API cover only
user-directed parallelization, wherein the user evidently
specify the performance to be taken by the compiler and
runtime system in order to perform the program in parallel.
OpenMP-compliant implementations are not required to check
for dependency, conflict, deadlocks, race conditions, or other
problems that effect from non-conforming programs. The user
Create the compliant program for using OpenMP. OpenMP
does not cover compiler produce continual parallelization and
orders to the compiler to help parallelization. The Fig.6 shows
that Fork- join model in OpenMP.[17](Fig.6)
Figure (6) fork join model in OpenMP
PCG algorithms parallelize using the OpenMP. The reason is
that OpenMP supports incrementally parallelization. That
means, the common of the serial code is not changed and the
user only needs to identify and parallelize just the most timeconsuming parts of the code, which are usually apply in loop.
7. MESSAGE PASSING INTERFACE (MPI)
Basically, it consists of the parallel programming. Message
passing interface is a programming concept. Parallel
computers basically use for the Network of Workstations.
Various machines across the degree of portability, the major
goal of message passing interface. Between the any pair of
MPI provides message passing. Set of process of
communication pattern used for graph. Graph consists of
nodes, edges. Where node is represented as process and edges
represented as connects the process. [10]
Figure (7) MPI
It is a process communicating through messages. Above figure
(fig (7)) shows the communication of message passing. Public
domain systems demonstrated – MPI can be efficiently and
portably implemented. Each vendor has implemented its own
variant. Molecular dynamics simulations are used in order to
Retrieve information about the processes which take place
inside an atomistic system that cannot be accessed through the
experiments. For large systems, the simulations can take a lot
of time and this is why MPI parallelization is used in order to
process data in less time. When working with a large set of
data which is subject to an algorithm, simulations can take a
lot of time. A solution of this matter is the MPI parallelization
makes the processing of data in a small amount of time.[10]
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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)
(I-SMAC 2017)
8. CONCLUSION
In this paper studied the shortest path algorithm to solve the
all pair shortest path and single source shortest path of graph.
Finding minimum spanning tree of graph studied the two
sequential algorithms. Graph calculates the single source
shortest path and all pair shortest path using two serial
algorithms, it can be converted into parallel which resulting
the number of iterations reduce consisting less time. Based on
two sequential algorithms Floyd and Dijkstra’s find the serial
implementation. Then using OpenMP and MPI convert the
serial code into parallel as less amount of time based on the
speedup factor and efficiency and cost. Initial stage of above
two algorithms is serial implementation. Further implement
will parallelize the above two algorithm and its comparison.
9. FEATURE SCOPE
By using OpenMP consuming or reducing the execution time
for designing the parallel programs, so large problems can be
solve in less amount of time. As well as compute the
performance evaluation parameter speedup and efficiency
based on the algorithms.
[12] Qiang Peng, Yulian Yu and Wenhong Wei, “The Shortest Path Parallel
Algorithm on Single Source Weighted Multi-level Graph”, 2009 Second
International Workshop on Computer Science and Engineering, 2009.
[13] Fang Zhou Lin, Nan Zhao, „Parallel Implementation of Dijkstra's
Algorithm“ , pp COP5570, 1999.
[14] Dandan Li, Xiaohui Ji, Qun Wang”A Parallel PCG Solver for Largescale Groundwater Flow Simulation Based on OpenMP ”, Fourth
International Symposium on Parallel Architectures, Algorithms and
Programming, p306-p309,2011.
[15] P.Harish, P.J. Narayan. "Accelerating Large Graph Algorithms on the
GPU Using CUDA". HiPC (2007). Volume 4873, pi 97-208,2007.
[16] R. W. Floyd. Algorithm 97: Shortest path. Commun. ACM, 5(6):345,
1962.
[17] Dandan Li, Xiaohui Ji, Qun Wang, “A Parallel PCG Solver for Largescale Groundwater Flow Simulation Based on OpenMP”, Fourth
International Symposium on Parallel Architectures, Algorithms and
Programming,pp306-309,2011.
[18] C. Demetrescu and G. Italiano,“A new approach to dynamic all pairs
shortest paths,” Journal of the ACM, vol. 51, no. 6, pp. 968–992, 2004.
[19] R. Hassin and E. Zemel, “On shortest paths in graphs with random
weights,” Mathematics of Operations Research, vol. 10, no. 4, pp. 557–
564, 1985.
[20] A. Frieze and G. Grimmett, “The shortest-path problem for graphs with
random arc-lengths, ”Discrete Applied Mathematics, vol. 10, pp. 57–77,
1985.
[21] Da-chuan Wei, ”Implementation of Route Selection Function Based on
Improved Floyd Algorithm”, WASE International Conference on
Information Engineering,pp 223-227,2010.
10. REFERENCES
[1]
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Implementation for Solving the Large Single Source-Shortest-Path
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[2] Quoc-Nam Tran,” Designing Efficient Many-core Parallel Algorithms
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[3] Nicolae Goga, Oludele Awodele,”Improved GROMACS Algorithms
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[4] Yulian Yu, Wenhong Wei ”The Shortest Path Parallel Algorithm on
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[5] Osama G. Attia, Alex Grieve, Kevin R. Townsend, Phillip Jones, and
Joseph Zambreno ,”Accelerating All-Pairs Shortest Path Using a
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[6] Danny Z. Chen, “Solving the All-Pair Shortest Path Problem on Interval
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[7] Uri Zwick, Yuval Peres,”All-Pairs Shortest Paths in O(n2) time with
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[8] B.Neelima , Rithesh G. Prabhu and Anjjan S Narayan,”Yet Another
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[9] D.S Banejee, S.Sharma, K. Kothapalli. Work Efficient Parallel
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[10] Clay P. Breshears, ”From Shared to Distributed Memory: Converting
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[11] Y. Zhao, Y. Han, Z. Fan, F. Qiu, Y.-C. Kuo, A. E. Kaufman, and K.
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