'Small instances' diaporamas de présentation

Data-Intensive Scalable Science: Beyond MapReduce

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Tower of Hanoi

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Induction

Induction. Induction. Induction is the name given to a problem solving technique based on using the solution to small instances of a problem to solve larger instances of a problem. In fact we can solve problems of arbitrary size. Size.

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Case study 3: orthogonal Latin squares

Case study 3: orthogonal Latin squares. Modelled by Barbara Smith. Modelling decisions. Many different ways to model even simple problems Combining models can be effective Channel between models Need additional constraints Symmetry breaking Implied (but logically) redundant.

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Lecture 34

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Divide And Conquer

Divide And Conquer. A large instance is solved as follows: Divide the large instance into smaller instances. Solve the smaller instances somehow. Combine the results of the smaller instances to obtain the result for the original large instance. A small instance is solved in some other way.

526 views • 35 slides

Divide And Conquer

Divide And Conquer. Distinguish between small and large instances. Small instances solved differently from large ones. Small And Large Instance. Small instance. Sort a list that has n <= 10 elements. Find the minimum of n <= 2 elements. Large instance.

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Windows Azure Compute

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Towards Predictable Datacenter Networks

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Cura : A Cost-optimized Model for MapReduce in a Cloud

Cura : A Cost-optimized Model for MapReduce in a Cloud. Balaji Palanisamy , Aameek Singh, Ling Liu and Bryan Langston. Data Growth. Worldwide Corporate Data Growth. 80 % of data growth:. IT.com analyses the. unstructured data. Exabytes. Source: IDC, The Digital Universe 2010.

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Our Objectives

Our Objectives. Explore the applicability of Microsoft technologies to real world scientific domains with a focus on data intensive applications Expect data deluge will demand multicore enabled data analysis/mining

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Hey, You, get Off of My Cloud: Exploring Information Leakage in Third-Party Compute Clouds

Hey, You, get Off of My Cloud: Exploring Information Leakage in Third-Party Compute Clouds. Presented by John Cain. A paper by Thomas Ristenpart , Eran Tromer , Hovav Shacham , and Stefan Savage,

247 views • 14 slides