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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. 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. (Those familiar with the subject will know that while compression waves will propagate through water and air, those media won't support shear waves.) A linear transform

SDSC Announces Scalable, High-Performance Data Storage Cloud SDSC Announces Scalable, High-Performance Data Storage Cloud Web-based System Offers High Durability, Security, and Speed for Diverse User Base September 22, 2011 By Jan Zverina The San Diego Supercomputer Center (SDSC) at the University of California, San Diego, today announced the launch of what is believed to be the largest academic-based cloud storage system in the U.S., specifically designed for researchers, students, academics, and industry users who require stable, secure, and cost-effective storage and sharing of digital information, including extremely large data sets. “We believe that the SDSC Cloud may well revolutionize how data is preserved and shared among researchers, especially massive datasets that are becoming more prevalent in this new era of data-intensive research and computing,” said Michael Norman, director of SDSC. “The SDSC Cloud marks a paradigm shift in how we think about long-term storage,” said Richard Moore, SDSC’s deputy director.

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. 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: A key technique used to achieve these goals is that any one node needs to coordinate with only a few other nodes in the system – most commonly, O(log n) of the participants (see below) – so that only a limited amount of work needs to be done for each change in membership. Structure[edit] and in the DHT, the SHA-1 hash of is generated, producing a 160-bit key to produce

NIST goes into detail on cloud's future, areas for improvement Jo Maitland, Senior Executive Editor Published: 04 Nov 2011 In a useful but long-winded set of documents aimed at furthering adoption of cloud computing , NIST has zeroed in on interoperability, security and portability as key areas for improvement. Kudos When you register, my team of editors will also send you alerts about public, private and hybrid cloud computing as well as other related technologies. to NIST for pushing the industry in the right direction, but did its "roadmap" report really require three volumes and over 200 pages? I feel like I just killed a tree printing it. Volume I: High Priority Requirements to Further U.S. Volume II: Useful Information for Cloud Adopters gets into the nitty-gritty of cloud security for government agencies, but notes that the industry is changing so fast it would be "premature" to offer definitive guidance around cloud security (see page 51). The first two volumes are open to the industry to comment on by December 2, 2011. Just kidding!

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. This introductory chapter provides relevant background information on Artificial Intelligence and Algorithms. What is AI Artificial Intelligence The field of classical Artificial Intelligence (AI) coalesced in the 1950s drawing on an understanding of the brain from neuroscience, the new mathematics of information theory, control theory referred to as cybernetics, and the dawn of the digital computer. Artificial Intelligence is therefore concerned with investigating mechanisms that underlie intelligence and intelligence behavior. Neat AI Scruffy AI Metaheuristics

AWS Elastic Beanstalk Amazon Web Services (AWS) comprises dozens of services, each of which exposes an area of functionality. While the variety of services offers flexibility for how you want to manage your AWS infrastructure, it can be challenging to figure out which services to use and how to provision them. With Elastic Beanstalk, you can quickly deploy and manage applications in the AWS cloud without worrying about the infrastructure that runs those applications. AWS Elastic Beanstalk reduces management complexity without restricting choice or control. You simply upload your application, and Elastic Beanstalk automatically handles the details of capacity provisioning, load balancing, scaling, and application health monitoring. Elastic Beanstalk uses highly reliable and scalable services that are available in the AWS Free Usage Tier. Elastic Beanstalk provides developers and systems administrators an easy, fast way to deploy and manage their applications without having to worry about AWS infrastructure.

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. For science, big data is the microscope of the 21st century Johns Hopkins is taking a $1.2 million grant from the National Science Foundation to build a 100 gigabit per second network to shuttle data from the campus to other large computing centers at national labs and even Google. The network will be capable of transferring an amount of data equivalent to 80 million file cabinets filled with text each day. The head of the project, Dr. The new data center at Johns Hopkins, awaiting its 100 Gbps backbone. That connection will be the 100 Gbps element funded by the NSF, and the Mid-Atlantic Crossroads network connects out to Pittsburgh and then onto Chicago via other 100 Gbps networks that are growing in number across the country. He ascribes this massive amount of data to the emergence of cheap compute, better imaging and more information, and calls it a new way of doing science. If that kind of data avalanche is a mere decade away, it appears our faster networks can’t come soon enough. Image courtesy of Flickr user RinzeWind.

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. If you wish for your contributed content to be retained, please add a statement to your user page that you release all your contributions under CC0 1.0, and inform me via Special:Emailuser/Dcoetzee. You can also re-add content that you created after the migration, provided that you are the sole author. At this time all article namespace content is already migrated. 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.

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. 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. Beyond these basic traversals, various more complex or hybrid schemes are possible, such as depth-limited searches such as iterative deepening depth-first search. Depth-first[edit] Pre-order[edit] In-order (symmetric)[edit] Post-order[edit] etc.

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]