PPT-Parallel and Distributed Algorithms

Overview Parallel Algorithm vs Distributed Algorithm PRAM Maximal Independent Set Sorting using PRAM Choice coordination problem Real world applications Introduction Need of distributed processing. We then consider the complications introduced by the introduction of parallelism and look at some proposed parallel frameworks Analysing Sequential Algorithms The design and analysis of sequential algorithms is a well developed 64257eld with a large Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr Ruan1unimasseyacnz brPage 2br Parallel algorithms for stereo vision 57521 shape from stereo ING IE R UAN X ENOPHON Institute of Information Mathematical Sciences Massey University at Albany Auckland New Zealand XingjieRuan1unimasseyacnz This paper Andrew Lumsdaine. Indiana University. lums@osl.iu.edu. My Goal in Life. Performance with elegance. Introduction. Overview of our high-performance, industrial strength, graph library. Comprehensive features. Improving Computer Performance. What performance translates into:. Time taken to do computation. Improving performance . → reducing time taken. What key benefits improving performance can bring:. Can solve “now-computationally-attainable” problems in . Jost Berthold. Simon Marlow. Abyd Al Zain. Kevin Hammond. The Parallel Haskell Landscape. research into parallelism using Haskell has been ongoing since the late 1980s. semi-implicit, deterministic programming model: . By Larry Hale and Trevor McCasland. Introduction to Genetic Algorithms. Genetic algorithms are search algorithms that use the principles of natural selection to find more optimal solutions to modeling, simulation, and optimization . 2. 　. Turing machine. 　. RAM (. Figure . ). 　. Logic circuit model. . RAM . (Random Access Machine). Operations . supposed to be executed in one unit time. (1). 　. Control operations such as. Dr. Yingwu Zhu. Chapter 27. Motivation. We have discussed . serial algorithms. that are suitable for running on a . uniprocessor. computer. We will now extend our model to . parallel algorithms. that can run on a . Guy E. Blelloch, Phillip B. Gibbons, Harsha Vardhan Simhadri. Slides by Endrias Kahssay. Why Parallel Cache Oblivious Algorithms? . Modern machines have multiple layers of cache – L1, L2, L3. . . Roughly 4 cycles, 10 cycles, and 40 cycles respectively. . Early Adopter: ASU - Intel Collaboration in Parallel and Distributed Computing Yinong Chen , Eric Kostelich , Yann -Hang Lee, Alex Mahalov , Gil Speyer, and Violet R. Syrotiuk 1 st NSF /TCPP Workshop on Parallel and Distributed Computing Education ( The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand We will Study …. Concurrency . Parallelism. Distributed computing. Evaluation. Assignments 40%, . Minor-1 15%, . Minor-2 15%, . Major 30%. Plagiarism is unacceptable. Offenders will be . penalized by a failing. Introduction. Papadakis Harris. Dept. of Engineering Informatics. ΤΕΙ . of Crete. Distributed Systems. - . Contents. Definition. Parallel. . Processing. Distributed . Computin. Clusters. Examples.