Parallel Depth-first Research Articles

Parallel path detection for fraudulent accounts in banks based on graph analysis.

This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected components, and using a parallel depth-first search algorithm to mark potentially fraudulent accounts. The algorithm is designed to fully exploit CPU resources and handle large-scale graphs with exponential growth. The performance of the algorithm is evaluated on various datasets and compared with serial time baselines. The results demonstrate that our approach achieves high performance and scalability on multi-core processors, making it a promising solution for detecting suspicious accounts and preventing money laundering schemes in the banking industry. Overall, our work contributes to the ongoing efforts to combat financial fraud and promote financial stability in the banking sector.

Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation

Operation optimization for large-scale offshore wind farms can cause the fatigue loads of single wind turbines to exceed their limits. This study aims to improve the economic profit of offshore wind farms by conducting multi-objective optimization via decoupled group operations of turbines. To do this, a large-scale wind farm is firstly divided into several decoupled subsets through the parallel depth-first search (PDFS) and hyperlink-induced topic search (HITS) algorithms based on the wake-based direction graph. Next, three optimization objectives are considered, including total output power, total fatigue load, and fatigue load dispatch on a single wind turbine (WT) in a wind farm. And then, the combined Monte Carlo and beetle swarm optimization (CMC-BSO) algorithms are applied to solve the multi-objective non-convex optimization problem based on the decentralized communication network topology. Finally, the simulation results demonstrate that the proposed method balances the total power output, fatigue load, and single fatigue loads with fast convergence.

Solving mazes with single-molecule DNA navigators.

Molecular devices with information-processing capabilities hold great promise for developing intelligent nanorobotics. Here we demonstrate a DNA navigator system that can perform single-molecule parallel depth-first search on a ten-vertex rooted tree defined on a two-dimensional DNA origami platform. Pathfinding by the DNA navigators exploits a localized strand exchange cascade, which is initiated at a unique trigger site on the origami with subsequent automatic progression along paths defined by DNA hairpins containing a universal traversal sequence. Each single-molecule navigator autonomously explores one of the possible paths through the tree. A specific solution path connecting a given pair of start and end vertices can then be easily extracted from the set of all paths taken by the navigators collectively. The solution path laid out on origami is illustrated with single-molecule imaging. Our approach points towards the realization of molecular materials with embedded computational functions operating at the single-molecule level.

Planar Strong Connectivity Helps in Parallel Depth-First Search

This paper proves that for a strongly connected planar directed graph of size $n$, a depth-first search tree rooted at a specified vertex can be computed in $O(\log^{5}n)$ time with $n/\log{n}$ processors. Previously, for planar directed graphs that may not be strongly connected, the best depth-first search algorithm runs in $O(\log^{10}n)$ time with $n$ processors. Both algorithms run on a parallel random access machine that allows concurrent reads and concurrent writes in its shared memory, and in case of a write conflict, permits an arbitrary processor to succeed.

Parallel Depth-First Search in General Directed Graphs

A directed cycle separator of an n-vertex directed graph is a vertex-simple directed cycle such that when the vertices of the cycle are deleted, the resulting graph has no strongly connected component with more than ${n / 2}$ vertices. It is shown that the problem of finding a directed cycle separator is in randomized NC. It is also proved that computing cycle separators and conducting depth-first search in directed graphs are deterministically NC-equivalent. These two results together yield the first randomized NC algorithm for depth-first search in general directed graphs.

Finding and Updating Depth-First Spanning Trees of Acyclic Digraphs in Parallel

The machine model used is a parallel random access machine that allows simultaneous access to the same memory location during only the read operation. The parallel depth-first search algorithm is based on a partial spanning tree doubling technique which is found to be useful in updating the depth-first spanning tree when a new node (edge) is added to an acyclic digraph. The most notable result is an O(log n) time parallel algorithm for updating the depth-first spanning tree solution when a new node (edge) is added to an n node acyclic digraph whose depth-first spanning tree is known

Parallel depth first search. Part II. Analysis

This paper presents the analysis of a parallel formulation of depth-first search. At the heart of this parallel formulation is a dynamic work-distribution scheme that divides the work between different processors. The effectiveness of the parallel formulation is strongly influenced by the work-distribution scheme and the target architecture. We introduce the concept of isoefficiency function to characterize the effectiveness of different architectures and work-distribution schemes. Many researchers considered the ring architecture to be quite suitable for parallel depth-first search. Our analytical and experimental results show that hypercube and shared-memory schemes are significantly better. The analysis of previously known work-distribution schemes motivated the design of substantially improved schemes for ring and shared-memory architectures. In particular, we present a work-distribution algorithm which guarantees close to optimal performance on a shared-memory/C-network-with-message-combining architecture (e.g. RP3). Much of the analysis presented in this paper is applicable to other parallel algorithms in which work is dynamically shared between different processors (e.g., parallel divide-and-conquer algorithms). The concept of isoefficiency is useful in characterizing the scalability of a variety of parallel algorithms.

Parallel depth first search. Part I. Implementation

This paper presents a parallel formulation of depth-first search which retains the storage efficiency of sequential depth-first search and can be mapped on to any MIMD architecture. To study its effectiveness it has been implemented to solve the 15-puzzle problem on three commercially available microprocessors - Sequent Balance 21000, the Intel Hypercube and BBN Butterfly. We have been able to achieve fairly linear speedup on Sequent up to 30 processors (the maximum configuration available) and on the Intel Hypercube and BBN Butterfly up to 128 processors (the maximum configurations available). Many researchers considered the ring architecture to be quite suitable for parallel depth-first search. Our experimental results show that hypercube and shared-memory architectures are significantly better. At the heart of our parallel formulation is a dynamic work distribution scheme that divides the work between different processors. The effectiveness of the parallel formulation is strongly influenced by the work distribution scheme and architectural features such as presence/absence of shared memory, the diameter of the network, relative speed of the communication network, etc. In a companion paper [16], we analyze the effectiveness of different load-balancing schemes and architectures, and also present new improved work distribution schemes.

A parallel search algorithm for directed acyclic graphs

A parallel algorithm for depth-first searching of a directed acyclic graph (DAG) on a shared memory model of a SIMD computer is proposed. The algorithm uses two parallel tree traversal algorithms, one for the preorder traversal and the other for therpostorder traversal of an ordered tree. Each of these traversal algorithms has a time complexity ofO(logn) whenO(n) processors are used,n being the number of vertices in the tree. The parallel depth-first search algorithm for a directed acyclic graphG withn vertices has a time complexity ofO((logn)2) whenO(n2.81/logn) processors are used.