Breadth-first search

About: Breadth-first search is a research topic. Over the lifetime, 1093 publications have been published within this topic receiving 21919 citations. The topic is also known as: BFS & breadth first search.

Depth-First Search and Linear Graph Algorithms

Abstract: The value of depth-first search or “backtracking” as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected componen...

Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data

Abstract: A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations

Scalable GPU graph traversal

Abstract: Breadth-first search (BFS) is a core primitive for graph traversal and a basis for many higher-level graph analysis algorithms. It is also representative of a class of parallel computations whose memory accesses and work distribution are both irregular and data-dependent. Recent work has demonstrated the plausibility of GPU sparse graph traversal, but has tended to focus on asymptotically inefficient algorithms that perform poorly on graphs with non-trivial diameter.We present a BFS parallelization focused on fine-grained task management constructed from efficient prefix sum that achieves an asymptotically optimal O(|V|+|E|) work complexity. Our implementation delivers excellent performance on diverse graphs, achieving traversal rates in excess of 3.3 billion and 8.3 billion traversed edges per second using single and quad-GPU configurations, respectively. This level of performance is several times faster than state-of-the-art implementations both CPU and GPU platforms.

Buffer placement in distributed RC-tree networks for minimal Elmore delay

Abstract: An algorithm is presented for choosing the buffer positions for a wiring tree such that the Elmore delay is minimal. For given required arrival times at the sinks of the wiring tree, the algorithm chooses buffers such that the required departure time at the source is as late as possible. The topology of the wiring tree, a Steiner tree, is assumed to be given, as well as the possible (legal) positions of the buffers. The algorithm uses a depth first search on the wiring tree to construct a set of time/capacitance pairs that correspond to different choices. The complexity of the algorithm is O(B/sup 2/), where B is the number of possible buffer positions. An extension of the basic algorithm allows minimization of the number of buffers as a secondary objective. >

Direction-optimizing breadth-first search

Abstract: Breadth-First Search is an important kernel used by many graph-processing applications. In many of these emerging applications of BFS, such as analyzing social networks, the input graphs are low-diameter and scale-free. We propose a hybrid approach that is advantageous for low-diameter graphs, which combines a conventional top-down algorithm along with a novel bottom-up algorithm. The bottom-up algorithm can dramatically reduce the number of edges examined, which in turn accelerates the search as a whole. On a multi-socket server, our hybrid approach demonstrates speedups of 3.3 -- 7.8 on a range of standard synthetic graphs and speedups of 2.4 -- 4.6 on graphs from real social networks when compared to a strong baseline. We also typically double the performance of prior leading shared memory (multicore and GPU) implementations.