Solution Search Simulation The Shortest Step On Chess Horse Using Breadth-First Search Algorithm

Horse seed in the chess board movement resembles the letter L. The chess pieces are one of a very hard-driven beans and seeds are often also the most dangerous if not carefully considered every movement. Simulation of this problem provides a chess board size n x n. Target (goal) of this problem is to move a horse beans of a certain position on a chess board position to the desired destination with the shortest movement simulates all possible solutions to get to the goal position. This problem is also one of the classic problems in artificial intelligence (AI). Settlement of this problem can use the help system and tree production tracking.Therefore, designed a simulation applications by utilizing several techniques of simulation programming and Breadth-First Search method. With this method, all nodes will be traced and the nodes at level n will be visited first before visiting the nodes at level n + 1. The purpose of this study is to design a software that is able to find all the solutions for the shortest movement toward the goal position by using the system of production and tracking tree.Results from this paper is that the software is able to find all solutions shortest movement a horse beans from the initial position to the goal position and displays the simulation of the movement of the horse in the chess board

DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications

Abstract not provide

Fast Iterative Graph Computation: A Path Centric Approach

Abstract—Large scale graph processing represents an inter-esting challenge due to the lack of locality. This paper presents PathGraph for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large graph using a collection of tree-based partitions and use an path-centric computation rather than vertex-centric or edge-centric computation. Our parallel computation model significantly improves the memory and disk locality for performing iterative computation algorithms. Second, we design a compact storage that further maximize sequential access and minimize random access on storage media. Third, we implement the path-centric computation model by using a scatter/gather programming model, which parallels the iterative computation at partition tree level and performs sequential updates for vertices in each partition tree. The experimental results show that the path-centric approach outperforms vertex-centric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs

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Department of Computer Science and EngineeringWith the advent of big data and social networks, large-scale graph processing becomes popular research topic. Recently, an optimization technique called Gorder has been proposed to improve the performance of in-memory graph processing. This technique improves performance by optimizing the graph layout on memory to have better cache locality. However, because the Gorder was designed with only the algorithms which have a specific I/O pattern, it is not suitable for some other graph algorithmsalso, the cost for applying the technique is significantly high. To solve the problem, we propose a new graph ordering called Neighborhood Ordering (N.Order). N.Order considers the characteristics of I/O accesses for SSDs and HDDs to improve the performance of disk-based graph engine. In addition, the algorithmic complexity of N.Order is simple compared to Gorder, hence it is cheaper to apply N.Order. N.Order reduces the cost of pre-processing up to 14 times compared to the Gorder, still its performance is 2 times faster compared to the random ordering.ope