Single-source shortest paths with the
                    parallel boost graph library

Edmonds, Nick; Breuer, Alexander; Gregor, Douglas; Lumsdaine, Andrew

doi:10.1090/dimacs/074/09

Cited by 25 publications

(20 citation statements)

References 41 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many papers available in literature involving graph algorithms [8,9,10,11,12,13,14]. Parallel shortest path algorithm was implemented in [15] for a super-computer.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Single Source Shortest Path Algorithm over Multiple GPUs in a Network of Workstations using OpenCL and MPI

Thouti¹,

Sathe²

2013

IJCA

View full text Add to dashboard Cite

Graphics Processing Units (GPUs) are being heavily used in various graphics and non-graphics applications. Many practical problems in computing can be represented as graphs to arrive at a particular solution. These graphs contains very large number, up to millions pairs of vertices and edges. In this paper, we present performance analysis of Dijkstra's single source shortest path algorithm over multiple GPU devices in a single machine as well as over a network of workstations using OpenCL and MPI. Experimental results prove that parallel execution of Dijkstra's algorithm has good performance when algorithm is run over multi-GPU devices in a single workstation as opposed to multi-GPU devices over a network of workstations. For our experimentation, we have used workstation having Intel Xeon 6-core Processor; supporting hyper-threading and a total of 24 threads with NVIDIA Quadro FX 3800 GPU device. The two GPU devices are connected by SLI Bridge. Overall, on average we achieved performance improvement up to an order of 10-15x.

show abstract

“…There are many papers available in literature involving graph algorithms [8,9,10,11,12,13,14]. Parallel shortest path algorithm was implemented in [15] for a super-computer.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Single Source Shortest Path Algorithm over Multiple GPUs in a Network of Workstations using OpenCL and MPI

Thouti¹,

Sathe²

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…A variety of performance results for parallel solutions to the single-source shortest paths problem have been presented [14], [24]. Parallel solutions to the all-pairs shortest paths problem have also been presented [21], [23].…”

Section: A Previous Workmentioning

confidence: 99%

A space-efficient parallel algorithm for computing betweenness centrality in distributed memory

Edmonds

Hoefler

Lumsdaine

2010

2010 International Conference on High Performance Computing

Self Cite

View full text Add to dashboard Cite

Abstract-Betweenness centrality is a measure based on shortest paths that attempts to quantify the relative importance of nodes in a network. As computation of betweenness centrality becomes increasingly important in areas such as social network analysis, networks of interest are becoming too large to fit in the memory of a single processing unit, making parallel execution a necessity. Parallelization over the vertex set of the standard algorithm, with a final reduction of the centrality for each vertex, is straightforward but requires Ω(|V | 2 ) storage. In this paper we present a new parallelizable algorithm with low spatial complexity that is based on the best known sequential algorithm. Our algorithm requires O(|V | + |E|) storage and enables efficient parallel execution. Our algorithm is especially well suited to distributed memory processing because it can be implemented using coarse-grained parallelism. The presented time bounds for parallel execution of our algorithm on CRCW PRAM and on distributed memory systems both show good asymptotic performance. Experimental results with a distributed memory computer show the practical applicability of our algorithm.

show abstract

“…The motivation arises from the poor performance of all aforementioned versions, which is due to their limited parallelism and excessive synchronization. Our goal is to coarsen the granularity of parallelism, as in [11,14,17], without, changing the algorithm itself. Thus, instead of partitioning the inner loop and assigning only a few neighbors to each thread, we seek to assign the relaxation of a complete set of neighbors to each thread.…”

Section: A Multithreaded Version Based On Htmentioning

confidence: 99%

“…Madduri et al [14] use ∆-stepping as the base algorithm on Cray MTA-2, an architecture that exploits fine-grain parallelism using hardware synchronization primitives, and achieve significant speedups. In the Parallel Boost Graph Library [11] Dijkstra's algorithm is parallelized for a distributed memory machine. The priority queue is distributed in the local memories of the system nodes and the algorithm is divided in supersteps, in which each processor extracts a node from its local priority queue.…”

Section: Related Workmentioning

confidence: 99%

Early experiences on accelerating Dijkstra's algorithm using transactional memory

Anastopoulos

Nikas

Goumas

et al. 2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

View full text Add to dashboard Cite

show abstract

Single-source shortest paths with the parallel boost graph library

Cited by 25 publications

References 41 publications

Performance Analysis of Single Source Shortest Path Algorithm over Multiple GPUs in a Network of Workstations using OpenCL and MPI

Performance Analysis of Single Source Shortest Path Algorithm over Multiple GPUs in a Network of Workstations using OpenCL and MPI

A space-efficient parallel algorithm for computing betweenness centrality in distributed memory

Early experiences on accelerating Dijkstra's algorithm using transactional memory

Contact Info

Product

Resources

About