Introduction to Parallel Programming and MapReduce - Google Code University - Google Code. Map reduce pattern. Graph traversal. Redundancy[edit] Unlike tree traversal, graph traversal may require that some nodes be visited more than once, since it is not necessarily known before transitioning to a node that it has already been explored. As graphs become more dense, this redundancy becomes more prevalent, causing computation time to increase; as graphs become more sparse, the opposite holds true. Thus, it is usually necessary to remember which nodes have already been explored by the algorithm, so that nodes are revisited as infrequently as possible (or in the worst case, to prevent the traversal from continuing indefinitely). This may be accomplished by associating each node of the graph with a "color" or "visitation" state during the traversal, which is then checked and updated as the algorithm visits each node.
Several special cases of graphs imply the visitation of other nodes in their structure, and thus do not require that visitation be explicitly recorded during the traversal. Depth-first search[edit] Tree traversal. Types[edit] Pre-order: F, B, A, D, C, E, G, I, H In-order: A, B, C, D, E, F, G, H, I Post-order: A, C, E, D, B, H, I, G, F Level-order: F, B, G, A, D, I, C, E, H Compared to linear data structures like linked lists and one-dimensional arrays, which have a canonical method of traversal (namely in linear order), tree structures can be traversed in many different ways. Starting at the root of a binary tree, there are three main steps that can be performed and the order in which they are performed defines the traversal type. The name given to a particular style of traversal comes from the order in which nodes are visited.
For the purpose of illustration, it is assumed that left nodes always have priority over right nodes. Depth-first traversal is easily implemented via a stack, including recursively (via the call stack), while breadth-first traversal is easily implemented via a queue, including corecursively. Depth-first[edit] Pre-order[edit] In-order (symmetric)[edit] Post-order[edit] Advantages: Welcome. Warning: LiteratePrograms is currently undergoing a license migration to Creative Commons CC0 1.0. All content will be erased unless its authors agree to release it under CC0.
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Based on Donald Knuth's concept of literate programming, LiteratePrograms is a collection of code samples displayed in an easy-to-read way, collaboratively edited and debugged, and all released into the public domain under the Creative Commons CC0 1.0 waiver (see Copyrights) so that anyone can use our code and text for any purpose without restriction. If you're interested in contributing your own programs, you can read about how to write an article. Introduction to Algorithms - Massachusetts Institute of Technology. Readings refer to chapters and/or sections of Introduction to Algorithms, 3rd Edition. See the table of contents. Introduction - Clever Algorithms. Welcome to Clever Algorithms! This is a handbook of recipes for computational problem solving techniques from the fields of Computational Intelligence, Biologically Inspired Computation, and Metaheuristics.
Clever Algorithms are interesting, practical, and fun to learn about and implement. Research scientists may be interested in browsing algorithm inspirations in search of an interesting system or process analogs to investigate. Developers and software engineers may compare various problem solving algorithms and technique-specific guidelines. Practitioners, students, and interested amateurs may implement state-of-the-art algorithms to address business or scientific needs, or simply play with the fascinating systems they represent. This introductory chapter provides relevant background information on Artificial Intelligence and Algorithms. The core of the book provides a large corpus of algorithms presented in a complete and consistent manner. What is AI Artificial Intelligence Neat AI.
Compression, Searching. Cataphora. MapReduce. Overview[edit] MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware).
Processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). MapReduce can take advantage of locality of data, processing it on or near the storage assets in order to reduce the distance over which it must be transmitted. "Map" step: Each worker node applies the "map()" function to the local data, and writes the output to a temporary storage. MapReduce allows for distributed processing of the map and reduction operations. Another way to look at MapReduce is as a 5-step parallel and distributed computation: Logical view[edit] Map(k1,v1) → list(k2,v2) Examples[edit]
Fun with Java, Understanding the Fast Fourier Transform (FFT) Algorithm. Java Programming, Notes # 1486 Preface Programming in Java doesn't have to be dull and boring. In fact, it's possible to have a lot of fun while programming in Java. This lesson is one in a series that concentrates on having fun while programming in Java. Viewing tip You may find it useful to open another copy of this lesson in a separate browser window. Supplementary material I recommend that you also study the other lessons in my extensive collection of online Java tutorials. General Discussion The purpose of this lesson is to help you to understand how the Fast Fourier Transform (FFT) algorithm works. There are several different FFT algorithms in common use. A general-purpose transform The Fourier transform is most commonly associated with its use in transforming time-domain data into frequency-domain data. Transforming from space domain to wave number domain For example, my first job after earning a BSEE degree in 1962 was in the Seismic Research Department of Texas Instruments.
Summary. Distributed hash table. Distributed hash tables History[edit] These systems differed in how they found the data their peers contained: Napster, the first large-scale P2P content delivery system to exist, had a central index server: each node, upon joining, would send a list of locally held files to the server, which would perform searches and refer the querier to the nodes that held the results. This central component left the system vulnerable to attacks and lawsuits. [citation needed]Gnutella and similar networks moved to a flooding query model – in essence, each search would result in a message being broadcast to every other machine in the network. While avoiding a single point of failure, this method was significantly less efficient than Napster. Distributed hash tables use a more structured key-based routing in order to attain both the decentralization of Freenet and gnutella, and the efficiency and guaranteed results of Napster.
Properties[edit] DHTs characteristically emphasize the following properties: and.