Search Results for « Single-source shortest paths »

We summarize the currently best known theoretical results for the single-source shortest paths problem for directed graphs with non-negative edge weights. We also point out that a recent result due to Cherkassky, Goldberg and Silverstein (1996) leads to even better time bounds for this problem than claimed by the authors.

Finding the shortest paths from a single source to all other vertices is a fundamental method used in a variety of higher-level graph algorithms. We present three parallel friendly and work-efficient methods to solve this Single-Source Shortest Paths (SSSP) problem: Work front Sweep, Near-Far and Bucketing. These methods choose different approaches to balance the trade off between saving work and organizational overhead. In practice, all of these methods do much less work than traditional Bellman-Ford methods, while adding only a modest amount of extra work over serial methods. These methods are designed to have a sufficient parallel workload to fill modern massively-parallel machines, and select reorganizational schemes that map well to these architectures. We show that in general our Near-Far method has the highest performance on modern GPUs, outperforming other parallel methods. We also explore a variety of parallel load-balanced graph traversal strategies and apply them towards our SSSP solver. Our work-saving methods always outperform a traditional GPU Bellman-Ford implementation, achieving rates up to 14x higher on low-degree graphs and 340x higher on scale free graphs. We also see significant speedups (20-60x) when compared against a serial implementation on graphs with adequately high degree.

The single-source shortest path problem arises in many applications, such as roads, social applications, and computer networks. Finding the shortest path is challenging, especially for graphs that contain a large number of vertices and edges. In this work, we propose a novel hybrid method that first sparsifies a given graph by removing most edges that cannot form the shortest path tree and then applies a classical shortest path algorithm to the sparser graph. Removing all the edges that cannot form the shortest path tree would be expensive since it is equivalent to solving the original problem. Therefore, we propose an iterative bioinspired algorithm, namely the Physarum algorithm, as the first stage to sparsify the graph. We prove that the resulting sparser graph always contains the shortest path tree of the original graph. Next, a state-of-the-art algorithm such as Dijkstra's is applied to find the single-source shortest path on the resulting graph. The proposed method is therefore a two-stage hybrid algorithm and it computes the single-source shortest path exactly. We compare the accuracy and solution time of the proposed hybrid method against state-of-the-art implementation of Dijkstra's algorithm and the BFS algorithm on directed weighted and unweighted graphs, respectively, as a baseline. The results show that the proposed hybrid method achieves a significant speed improvement compared to the baseline.

We devise new algorithms for the single-source shortest paths (SSSP) problem with non-negative edge weights in the CONGEST model of distributed computing. While close-to-optimal solutions, in terms of the number of rounds spent by the algorithm, have recently been developed for computing SSSP approximately, the fastest known exact algorithms are still far away from matching the lower bound of Ω (n + D) rounds by Peleg and Rubinovich [SIAM Journal on Computing 2000], where n is the number of nodes in the network and D is its diameter. The state of the art is Elkin's randomized algorithm [STOC 2017] that performs Õ(n^2/3 D^1/3 + n^5/6) rounds. We significantly improve upon this upper bound with our two new randomized algorithms for polynomially bounded integer edge weights, the first performing Õ(√n D) rounds and the second performing Õ(√n D^1/4 + n^3/5 + D) rounds. Our bounds also compare favorably to the independent result by Ghaffari and Li [STOC 2018]. As side results, we obtain a (1+ε)-approximation Õ((√n D^1/4+D)/ε)-round algorithm for directed SSSP and a new work/depth trade-off for exact SSSP on directed graphs in the PRAM model.

In this paper we propose the first experimental study of the fully dynamic single-source shortest-paths problem on directed graphs with positive real edge weights. In particular, we perform an experimental analysis of three different algorithms: Dijkstra's algorithm, and the two output bounded algorithms proposed by Ramalingam and Reps in [30] and by Frigioni, Marchetti-Spaccamela and Nanni in [18], respectively. The main goal of this paper is to provide a first experimental evidence for: (a) the effectiveness of dynamic algorithms for shortest paths with respect to a traditional static approach to this problem; (b) the validity of the theoretical model of output boundedness to analyze dynamic graph algorithms. Beside random generated graphs, useful to capture the "asymptotic" behavior of the algorithms, we also developed experiments by considering a widely used graph from the real world, i.e., the Internet graph.

We present a deterministic (1+o(1))-approximation O(n1/2+o(1)+D1+o(1))-time algorithm for solving the single-source shortest paths problem on distributed weighted networks (the CONGEST model); here n is the number of nodes in the network and D is its (hop) diameter. This is the first non-trivial deterministic algorithm for this problem. It also improves (i) the running time of the randomized (1+o(1))-approximation Õ(n1/2D1/4+D)-time algorithm of Nanongkai [STOC 2014] by a factor of as large as n1/8, and (ii) the O(є−1logє−1)-approximation factor of Lenzen and Patt-Shamir’s Õ(n1/2+є+D)-time algorithm [STOC 2013] within the same running time. Our running time matches the known time lower bound of Ω(n1/2/logn + D) [Das Sarma et al. STOC 2011] modulo some lower-order terms, thus essentially settling the status of this problem which was raised at least a decade ago [Elkin SIGACT News 2004]. It also implies a (2+o(1))-approximation O(n1/2+o(1)+D1+o(1))-time algorithm for approximating a network’s weighted diameter which almost matches the lower bound by Holzer et al. [PODC 2012].

In this paper we are going to present a teaching—learning method that help students look at three single-source shortest paths graph-algorithms from a so called "upperview": the algorithm based on the topological order of the nodes, the Dijkstra algorithm, the Bellman-Ford algorithm. The goal of the suggested method is, beyond the presentation of the algorithms, to offer the students a view that reveals them the basic and even the slight principal differences and similarities between the strategies. In order to succeed in this object, teachers should present the mentioned algorithms as cousin dynamic programming strategies.

Graphs are mathematical structures used in many applications. In recent years, many applications emerged that require the processing of large dynamic graphs where the graph’s structure and properties change constantly over time. Examples include social networks, communication networks, transportation networks, etc. One of the most challenging problems in large scale dynamic graphs is the single-source shortest path (SSSP) problem. Traditional solutions (based on Dijkstra’s algorithms) to the SSSP problem do not scale to large dynamic graphs with a high change frequency. In this paper, we propose an efficient SSSP algorithm for large dynamic graphs. We first present our algorithm and give a formal proof of its correctness. Then, we give an analytical evaluation of the proposed solution.

Computing single source shortest path is a popular problem in graph theory, extensively applied in many areas like computer networks, operation research and complex network analysis.SSSP is difficult to parallelize efficiently as more parallelization leads to more work done by any algorithm.MapReduce is a popular programming framework for large data processing in distributed and cloud environments.In this paper, we have proposed MR-DSMR, a Map reduce version of Dijkstra Strip-mined Relaxation (DSMR) algorithm and MR3-BFS algorithms.We have compared the performance of both the algorithms with BFS.It is observed that MR-DSMR takes lesser communication and computation time compared to existing algorithms.