A modified parallel approach to Single Source Shortest Path Problem for massively dense graphs using CUDA

Kumar, Sumit; Misra, Alok; Tomar, Raghvendra Singh

doi:10.1109/iccct.2011.6075214

Cited by 18 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[2] Add the first shortest path in the Zresultt. [3] for k=2 to K_no [4] for each edge of the (k-1)th path(s, v1, v2, v3….. vn) of Zresult [5] Remove each edge (vi , vi+1) from graph corresponding Zresult to where same sub path from s to vi [6] Apply shortest path algorithm for vi to t [7] Combine path s to vi and new path from vi to t [8] Store the calculated path in Zrest [9] End of for [10] Take minimum path from the REST path list and store in Zresult. [11] End of for End According to yen's algorithm firstly calculate the shortest path between given pair of vertices.…”

Section: Algorithm 1: Modified Yen's Algorithm (Graph G (V E W) Somentioning

confidence: 99%

See 1 more Smart Citation

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Singh¹,

Singh²

2015

Procedia Computer Science

View full text Add to dashboard Cite

K-shortest path algorithm is generalization of the shortest path algorithm. K-shortest path is used in various fields like sequence alignment problem in molecular bioinformatics, robot motion planning, path finding in gene network where speed to calculate paths plays a vital role. Parallel implementation is one of the best ways to fulfill the requirement of these applications. A GPU based parallel algorithm is developed to find k number of shortest path in a positive edge-weighted directed large graph. In calculated shortest path repetition of the vertices is not allowed. Implemented algorithm calculates a k-shortest path between two pair of vertices of a graph with n nodes and m vertices. This approach is based on Yen's algorithm to find k-shortest loopless path. We implemented our algorithms in Nvidia's GPU using Compute Unified Device Architecture (CUDA). This paper presents comparative analysis between CPU and GPU based implementation of Yen's Algorithm. Our approach achieves the 6 time speed up in comparison of serial algorithm.

show abstract

Section: Algorithm 1: Modified Yen's Algorithm (Graph G (V E W) Somentioning

confidence: 99%

“…The most popular algorithm for this problem is Dijkastra's algorithm 1 . Parallel implementation of Dijkastra's algorithm is given by different researchers [2][3][4][5][6][7][8][9] . Kshortest path problem is generalization of shortest path problem which calculates k number of shortest path in increasing order of weight.…”

Section: Introductionmentioning

confidence: 99%

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Singh¹,

Singh²

2015

Procedia Computer Science

View full text Add to dashboard Cite

show abstract

“…Whilst platforms such as nVidia's CUDA can provide some degree of abstraction for users, the monolithic nature of the memory may still pose a problem. Despite this, GPU platforms such as CUDA have been utilised to provide exceptionally fast network analysis, [5]. The power consumption costs of GPU architectures, can, however, be significant [6].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale On-Chip Dynamic Programming Network Inferences Using Moderated Inter-core Communication

Mundy

Mak

Yakovlev

et al. 2012

2012 12th International Conference on Application of Concurrency to System Design

View full text Add to dashboard Cite

The analysis of large scale, complex networks using dynamic programming is of great use in many scientific and engineering disciplines. Current applications often require the analysis of scale-free networks with many millions of nodes and edges; presenting a huge computational challenge. Employing a distributed networkson-chip infrastructure presents a unique opportunity of delivering power efficient and massive parallel accelerations. However, bursting and asymmetric communications across cores could create instant network saturation and lead to packet loss and performance degradation. In this paper, we present a moderated communication methodology that enables a balanced channel usage and network topological adaptation for improved performance. A novel analytical communication model for NoC is developed and leads to a theoretical bound of the on-chip communication cost estimate. Performances of the many-core computation and the proposed methods are rigorously evaluated using the real 18-core SpiNNaker chip. We demonstrate a 10x speed-up in analysis convergence and a 42% reduction in instantaneous Packet Injection Rate based on benchmark networks.

show abstract

“…Aydın Buluc, John R. Gilbert and Ceren Budak [8] have proposed parallel implementations for SSSP and APSP using CUDA. A CUDA implementation for Bellman_Ford is given in [13] and by making algorithm suitable for parallelism they have got speedup of about 10x.…”

Section: Related Workmentioning

confidence: 99%

“…Here barrier function (CL_GLOBAL_MEM_FENCE) is used on kernel side so as to order the read and write operations to and from global memory [13].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Parallel Implementations for Solving Shortest Path Problem using Bellman-Ford

Hajela¹,

Pandey²

2014

IJCA

View full text Add to dashboard Cite

In this paper, different parallel implementations of BellmanFord algorithm on GPU using OpenCL are presented. These variants include Bellman-Ford for solving single source shortest path (SSSP) having two variants and Bellman-Ford for all pair shortest path (APSP) problems. Also, a comparative analysis of their performances on CPU and GPU is discussed in this paper.Write-write consistency in BellmanFord is overcome using synchronization mechanism available in OpenCL and by explicit synchronization by modifying the algorithm.An average speed up of 13.8x for parallel bellman ford for SSSP and an average speed up of 18.5x for bellman ford for APSP is achieved by proposed algorithm.

show abstract

A modified parallel approach to Single Source Shortest Path Problem for massively dense graphs using CUDA

Cited by 18 publications

References 8 publications

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Large-Scale On-Chip Dynamic Programming Network Inferences Using Moderated Inter-core Communication

Parallel Implementations for Solving Shortest Path Problem using Bellman-Ford

Contact Info

Product

Resources

About