Breadth-first Search Algorithm Research Articles

ScalaBFS2: A High-performance BFS Accelerator on an HBM-enhanced FPGA Chip

The introduction of High Bandwidth Memory (HBM) to the FPGA chip makes it possible for an FPGA-based accelerator to leverage the huge memory bandwidth of HBM to improve its performance when implementing a specific algorithm, which is especially true for the Breadth-First Search (BFS) algorithm that demands a high bandwidth for accessing the graph data stored in memory. Different from traditional FPGA-DRAM platforms where memory bandwidth is the precious resource due to the limited DRAM channels, FPGA chips equipped with HBM have much higher memory bandwidths provided by the large quantities of HBM channels, but still a limited amount of logic (LUT, FF, and BRAM/URAM) resources. Therefore, the key to design a high-performance BFS accelerator on an HBM-enhanced FPGA chip is to efficiently use the logic resources to build as many as possible Processing Elements (PEs) and configure them flexibly to obtain as high as possible effective memory bandwidth that is useful to the algorithm from the HBM, rather than partially emphasizing the absolute memory bandwidth. To exploit as high as possible effective bandwidth from the HBM, ScalaBFS2 conducts BFS in graphs in a vertex-centric manner and proposes designs, including the independent module (HBM Reader) for memory accessing, multi-layer crossbar, and PEs that implement hybrid mode (i.e., capable of working in both push and pull modes) algorithm processing, to utilize the FPGA logic resources efficiently. Consequently, ScalaBFS2 is able to build up to 128 PEs on the XCU280 FPGA chip (produced with the 16 nm process and configured with two HBM2 stacks) of a Xilinx Alveo U280 board and achieves performance of 56.92 Giga Traversed Edges Per Second (GTEPS) by fully using its 32 HBM memory channels. Compared with the state-of-the-art graph processing system (i.e., ReGraph) built on top of the same board, ScalaBFS2 achieves 2.52x~4.40x performance speedups. Moreover, when compared with Gunrock running on an Nvidia A100 GPU that is produced with the 7 nm process and configured with five HBM2e stacks, ScalaBFS2 achieves 1.34x~2.40x speedups on absolute performance, and 7.35x~13.18x speedups on power efficiency.

Energy-Aware Positioning Service Provisioning for Cloud-Edge-Vehicle Collaborative Network Based on DRL and Service Function Chain

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

Formula omitted]piler: Compilers in search of compilations

Compilers pose significant challenges in their development as software products. Language developers face the complexities of ensuring efficiency, adhering to good design practices, and maintaining the overall codebase. These factors make it difficult to predict the unexpected impact of updates on existing software built on the current compiler stack. Furthermore, software created for a specific compiler often lacks reusability for other compiler environments. In this study, we propose a comprehensive framework for the uniform development of compilers that addresses these issues. Our approach involves developing compilers as a collection of small transpilation units, referred to as deltas. The transpilation infrastructure takes source code written in a particular source language and searches for a path of deltas to generate equivalent source code in the target language. By adopting this methodology, language developers can easily update their languages by introducing new deltas into the system. Existing code remains unaffected as old transpilation paths remain available. To support this framework, we have devised a metric space for efficient delta search. This metric space enables us to define a non-overestimating heuristic function, which proves valuable in solving the search problem. Leveraging the A* search algorithm, we can efficiently transpile programs from a source language to the target language. To evaluate the effectiveness of our approach, we conducted a benchmark comparison between the A* search algorithm and the simpler breadth-first search (BFS) algorithm. The benchmark consisted of over 100 transpilation searches, providing valuable insights into the performance and capabilities of this framework.

Research and Application of Power Grid Data Anomaly Tracing Method Based on Time Series Correlation

Abstract Power grid data is a “barometer” that reflects the operational trend of the power grid system, so conducting a traceability study on abnormal data generated by the power grid system is crucial to maintaining the stable operation of the power grid system and preventing further malfunctions. This article proposes a maximum time series correlation ring tracing algorithm based on time series correlation: firstly, for a large amount of time series data collected from measurement points in the power grid system, a correlation coefficient matrix is calculated, and then a time series correlation graph is constructed using graph theory knowledge. The Kruskal algorithm is used to search for the longest spanning tree in the time series correlation graph. Finally, based on the spanning tree, the breadth-first search (BFS) algorithm is further used to obtain the maximum time series correlation ring[1]. By using the time series correlation of each node within the ring and the physical topological relationship between nodes, the abnormal data can be traced back. Distinguish whether the generation of abnormal data is due to a single point failure or system failure. Most of the existing fault tracing techniques are based on machine learning algorithms, which are difficult to widely apply in high-dimensional time series data due to their high complexity.Through experiments on real power grid data, the results verify the effectiveness of this method in anomaly tracing of high-dimensional time series data. Through comparative experiments, this method is superior to machine learning model algorithms in terms of efficiency and stability. At the same time, this method does not require additional calculations of the statistical distribution of the samples to be tested, thus greatly saving computational costs.

A parallel Breadth-First Search using shared memory level-synchronization

Breadth-first search (BFS) stands as a cornerstone in graph exploration techniques, enabling systematic traversal of a provided graph. As the digital era continues to burgeon, there has been a marked upswing in the need to process vast graph-based data sets. To harness the power of such data effectively, it becomes imperative to use computational tools efficiently. Parallelizing BFS emerges as a pivotal strategy in this regard, leveraging the expansive capabilities of multiprocessor systems to maximize efficiency. This manuscript introduces a level-synchronous parallel BFS that is predicated on the shared-memory model. Recognizing the potential pitfalls of such an approach, especially regarding overhead induced by implicit barriers and critical sections, meticulous optimization techniques are infused into the model. These strategies are not mere afterthoughts; they are woven into the fabric of the design, ensuring smooth operations even when scaled. To validate the efficacy of this model, a rigorous assessment is carried out using the Graph500 benchmark. This offers insights into the performance scale of the parallel BFS algorithm, especially focusing on its speedup in relation to the number of operational threads. Concluding this exploration, the paper delineates prospective avenues for refining and further enhancing the proposed parallel implementation, aiming for even greater efficiencies in future endeavors.

Optimization of Chlorine Injection Schedule in Water Distribution Networks Using Water Age and Breadth-First Search Algorithm

Chlorine decay over time and distance travelled poses challenges in maintaining consistent chlorine levels from treatment plants to demand nodes in water distribution networks (WDNs). Many studies have focused on optimizing chlorine booster systems and addressing dosage and location. This study proposes a chlorine injection optimization model for maintaining spatial and temporal chlorine residuals within an acceptable range. First, the approach involves identifying potential pathways from the source to demand nodes using a breadth-first search (BFS) algorithm. Subsequently, the required chlorine injection to maintain a 0.2 mg/L residual chlorine level at demand nodes is estimated based on water age. Finally, a single-objective genetic algorithm optimizes the chlorine injection schedule at the source. The results demonstrated that chlorine estimation based on water age exhibited promising results with an average error below 10%. In addition, the four-interval injection scheme performed well in adapting to changing demand patterns, making the method robust to varying demand patterns. Moreover, the model could accommodate fluctuating water temperature conditions according to operating seasons. This study provides valuable insights into effectively managing chlorine levels and operations of WDNs, and paves the way for using water age for chlorine estimation.

Evaluation of Shortest Path by using Breadth-First Algorithm under Neutrosophic Environment

This paper recommends and designs concepts for evaluating the shortest path (SP) for a connected network using a modified breadth-first search algorithm in an uncertain environment. Evaluating the SPs of a network is an essential and extensively encountered optimization problem. Here we develop a new method for determining the SP in a neutrosophic environment in which the arc lengths are uncertain. Here, we use the parameters as neutrosophic numbers, and the new methodology, i.e., canonical representation of neutrosophic numbers in a neutrosophic environment, is used to convert neutrosophic edge lengths to crisp edge lengths and enhance the traditional breadth-first search algorithm. We employ this concept to accommodate the ambiguities in subjective decisions and address these issues. Here we demonstrate that the proposed algorithm has good stability and high efficiency for evaluating the SP. Finally, a numerical example is provided to explain the suggested algorithm.

Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines’ affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies: A Double Layer Network Approach

High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains' operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains' operation strategy and the systems' constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

Reward bonuses with gain scheduling inspired by iterative deepening search

This paper introduces a novel method of adding intrinsic bonuses to task-oriented reward function in order to efficiently facilitate reinforcement learning search. While various bonuses have been designed to date, this paper points out that the intrinsic bonuses can be analogous to either of the depth-first or breadth-first search algorithms in graph theory. Since these two have their own strengths and weaknesses, new bonuses that can bring out the respective characteristics are first proposed. The designed bonuses are derived as differences in key indicators that converge in the steady state based on the concepts of value disagreement and self-imitation. Then, a heuristic gain scheduling is applied to the designed bonuses, inspired by the iterative deepening search, which is known to inherit the advantages of the two search algorithms. The proposed method is expected to allow agent to efficiently reach the best solution in deeper states by gradually exploring unknown states. In three locomotion tasks with dense rewards and three simple tasks with sparse rewards, it is shown that the two types of bonus contribute to the performance improvement of the different tasks complementarily. In addition, by combining them with the proposed gain scheduling, all tasks can be accomplished with high performance.

Research on Seismic Connectivity Reliability Analysis of Water Distribution System Based on CUDA

To improve the seismic connectivity reliability (SCR) analysis efficiency of water distribution systems (WDS) based on Monte Carlo (MC) simulation, the quasi-Monte Carlo (QMC) method sampled by a low-discrepancy sequence is applied. Furthermore, a parallel algorithm combined with the breadth-first search algorithm for SCR analysis of WDS based on the QMC method and Compute Unified Device Architecture (CUDA) platform was proposed. A city WDS was taken as a computational example, the accuracy and efficiency of the traditional MC algorithm and parallel algorithm were compared, and the influence of the Sobol sequence and pseudo-random number sequence was analysed. The analysis results show that when 1,000,000 simulations are performed, the maximum error of the calculation results of the two methods is 0.2%, and the parallel method can obtain a six-fold speedup ratio compared with the serial method, indicating that the proposed parallel method is correct, meets the accuracy requirements, and helps to improve the SCR analysis efficiency. When the number of simulations is the same, the simulation results based on the Sobol sequence are more accurate than those based on the pseudo-random number sequence. The proposed parallel method also achieves a good acceleration effect in the SCR analysis of large-scale WDS.

Research of the Properties of the Breadth-First Search Algorithm for Finding the Movement Route of Robots

Purpose of research. The research presented in this article is aimed at improving the speed of finding a path for the movement route of robots. The scientific novelty is the obtained correlation of time and field size.Methods. To find the path in the maze, the depth-first search and breadth-first search algorithms were used, the basis of which is the cyclic processing of adjacent previously unvisited graph vertices. Performance is estimated in terms of the speed of program code execution on prepared samples. Scientific novelty was obtained by studying the influence of map sizes on the performance of depth-first and breadth-first search algorithms.Results. A software implementation of breadth-first and depth-first search algorithms has been developed. The article provides a more detailed description of the breadth-first search algorithm in the form of pseudo and program codes, which are based on the while loop, where the queue of checked graph vertices is processed. Based on the evaluation of the speed of the found path, it was concluded that the breadth-first search is not the fastest. Based on the assessment of the influence of various factors on the speed of the algorithm, it was concluded that an increase in the size of the field, a decrease in the number of obstacles and a distance between the starting and final points increases the execution time of the algorithm.Conclusion. The breadth-first search algorithm and its software implementation were presented. In the course of experimental studies, it was found that this algorithm is not the fastest in time, but in all tests, it found the shortest path. The correlation ta = f(w, h) was also obtained for the prepared samples of the desired field, which is expressed as the dependence of the algorithm execution time on the length and width of the field. And we can conclude that it is applicable for finding the movement path of robots, since it always finds the shortest path.

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

Ever since the human society has entered the era of big data, the quantity and type of digital learning resources on the Internet are increasing exponentially, and the requirement of students for learning resource retrieval is on the rise. However, the existing methods of learning resource retrieval generally overlook the overall knowledge systems that the students have already possessed, so it’s impossible for them to predict the students’ learning path or perform deviation adjustment. In view of these issues, this paper aims to study a new learning resource retrieval method based on multi-knowledge association mining. At first, the paper introduces the application of the Knowledge Graph Embedding (KGE) technology in learning resource retrieval, proposes the problem of learning resource retrieval, and points out the goal of learning resource retrieval. Then, a breadth-first soft-matching search algorithm is introduced to attain the multiple association paths between students and learning resources, and a retrieval module is constructed based on association path to further learn the association paths between students and learning resources within the framework of feature learning, and to predict the probability of interactions between students and learning resources. At last, this paper evaluates the association paths and uses experimental results to verify the validity of the proposed learning resource retrieval method.

FIImap: Fast Incremental Inflate Mapping for Autonomous MAV Navigation

Three-dimensional mapping is an essential component of autonomous Micro Aerial Vehicle (MAV) navigation. The paper focuses on the 3D spatial representation method of MAV to overcome the collision problem caused by soft constraints, control error, and planning with the center of mass by inflating the occupancy grid map. A fast incremental inflated map construction method is proposed, which reduces the time-consumption caused by the increase of map range and inflated size. The method focuses on areas of the map that occupied state changes and introduces two arrays that record newly appearing and disappearing obstacles. Then, a series of breadth-first search algorithms are used to traverse the parts of the inflated map that need local modification to update the inflated map. Moreover, a sliding map model is designed based on the MAV position, which is suitable for large-range autonomous flight. The effectiveness of the proposed approach is verified with simulated and actual flight data. The proposed method takes about 3 ms to construct the inflated map with a local update range of 16 m × 16 m × 6 m.

An Adaptive Routing Algorithm for Inter-Satellite Networks Based on the Combination of Multipath Transmission and Q-Learning

In a satellite network, the inter-satellite link can facilitate the information transmission and exchange between satellites, and the packet routing of the inter-satellite link is the key development direction of satellite communication systems. Aiming at the complex topology and dynamic change in LEO satellite networks, the traditional single shortest path algorithm can no longer meet the optimal path requirement. Therefore, this paper proposes a multi-path routing algorithm based on an improved breadth-first search. First, according to the inter-satellite network topology information, the improved breadth-first search algorithm is used to obtain all the front hop node information of the destination node. Second, all the shortest paths are obtained by backtracking the path through the front hop node. Finally, according to the inter-satellite network, the bandwidth capacity of the traffic and nodes determines the optimal path from multiple shortest paths. However, due to the high dynamics of low-orbit satellite networks, the topology changes rapidly, and the global topology of the network is often not available. At this time, in order to enhance the adaptability of the algorithm, this paper proposes an inter-satellite network dynamic routing algorithm based on reinforcement learning. Verified by simulation experiments, the proposed algorithm can improve the throughput of the inter-satellite network, reduce the time delay, and the packet loss rate.

Analisis Perbandingan Finite State Automata (FSA) Dengan Breadth First Search (BFS) Terhadap Desain Sistem Aplikasi Screening Awal Karyawan

This study aims to achieve a business process needed by a company in implementing the company's vision and mission. Therefore, a selection process is needed to be used by the company as a criterion to meet company standards. So this research is based on the implementation of the initial screening of too many employees so that HRD work is hampered. Thus, this application is built using the BFS method. We tried to analyze the implementation of the employee recruitment screening algorithm by comparing the BFS and FSA methods. In addition to building an application using the BFS method, the author also built it using the FSA method to see which of these two methods is more accurate to use in the initial employee screening selection. So the method we are currently using is the Breadth-First Search (BFS) method because it can describe the process starting from the initial selection of employees by explaining the registration steps to the final process, namely decisions from predetermined screening results. So the expected result is creating an application to help HRD as an initial selection of employee recruitment. The results obtained in this study are between the Breadth-First Search Algorithm (BFS) and the Finite State Automata (FSA) method. Employee start.Keywords : Screening, Employee, Recruitment, FSA (Finite State Automata)

Improving the routing performance in optical networks by an optimised BFS algorithm

The procedure of routing and wavelength assignment became a limiting factor for new applications in Optical Networks (ONs) due to the faster dynamic and greater unpredictability of service requests. The need for quick service recovery in the event of failures is also a crucial aspect which depends on this procedure. The present work proposes a new assignment of alternative routes able to comply to any data service request in ONs. The assignment procedure is based on the distinction by low correlation between paths, on the available bandwidth of different paths and on the latency for the establishment of communication between network nodes. In such context, an improvement of the Breadth-First Search (BFS) algorithm is proposed and tested with parameters from a real-world system, resulting in a good performance to find alternative routes and to classify them according to the best score obtainable, independently of the source or destiny nodes.

Commodity Search Based on the Hybrid Breadth-Depth Algorithm in the Crowd Intelligence Based Transaction Network

Crowd intelligence based transaction network (CIbTN) is a new generation of e-commerce. In a CIbTN, buyers, sellers, and other institutions are all independent and intelligent agents. Each agent stores the commodity information in a local node. The agents interconnect through a circle of friends and construct an unstructured network. To conduct the commodity search task in a network more efficiently and in an energy-saving manner when a buyer presents a commodity demand, a hybrid breadth-depth search algorithm (HBDA) is proposed, which combines the search logic of the breadth-first search algorithm and the depth-first search algorithm. We defined the correlation degree of nodes in a network, optimized the rules of search and forwarding paths using the correlation degree between a node and its neighboring nodes in the circle of friends, and realized the HBDA based on the PeerSim simulation tool and Java. Experimental results show that, in general, the proposed HBDA has a better search success rate, search time, commodity matching degree, and search network consumption over the two blind search algorithms. The HBDA also has good expansibility, thus allowing it to be used for commodity search efficiently with a high success rate in large-scale networks.

Detector Processor for a 5G Base Station.

Nonlinear soft bit detection is essential for the uplink receivers of 5G base stations, especially for users around the cell edge. However, its throughput and computing complexity are always challenges for both research and industry. A low-cost and low-power parallel implementation of a soft-output detector based on sorted QR decomposition (SQRD) and the K-best breadth-first search algorithm is thus proposed to reduce computational complexity and latency. In addition, to save area and reduce latency, two improvement methods are used: (1) reduce the computing cost by saturating and truncating large values during PED computing and (2) reduce the sorting cost by using the binary bit sorting method for a reduced sample set with finite accuracy. Furthermore, a pipelined VLSI architecture is designed using 28-nm digital CMOS technology offered by Semiconductor Manufacturing International Corporation (SMIC). It can achieve a peak throughput of 6400 Mbps while consuming 153 K gates (including all flip-flops) for SMIC’s 28-nm technology and running at 800 MHz, a 32% cost reduction compared with the published reference design.