PPT-On Centralized Sublinear Algorithms and Some Relations to Distributed Computing

Dana Ron TelAviv University ADGA October 2015 Efficient Centralized Algorithms Usually when we say that an algorithm is efficient we mean that it runs in time polynomial in the input size . It enables analytics and knowledge generation to occur at the source of the data This approach requires leveraging resources that may not be continuously connected to a network such as laptops smartphones tablets and sensors Edge Computing covers a Uni processor computing can be called centralized computing brPage 3br mainframe computer workstation network host network link terminal centralized computing distributed computing A distributed system is a collection of independent computers interc David Ríos. CSCI 6175. Fall 2011. Why are we going to spend 25 minutes on Condor?. Agenda. What is Condor?. Who can benefit from Condor?. Distributed Computer Concepts. Condor Design Philosophy. How Condor Works. PropagationModelsforTrustandDistrustinSocialNetworks Centralized Distributed Centralized Group Group Scalar Local Global Trust Metric Features ig.2.rustmetricclassi Talya Eden, . Tel Aviv . University. Amit Levi, . University of Waterloo. Dana . Ron, . Tel Aviv . University. C. . Seshadhri. , . UC Santa Cruz. Counting Triangles. Basic graph-theoretic algorithmic . Dana Ron . Tel-Aviv University. ADGA, October 2015. Efficient (Centralized) . Algorithms. Usually, when we say that an algorithm is . efficient . we mean that it runs in time . polynomial. in the input size . Set up a large number of machines all identically configured. Connect them to a high speed LAN. And to the Internet. Accept arbitrary jobs from remote users. Run each job on one or more nodes. Entire facility probably running mix of single machine and distributed jobs, simultaneously. Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. Kalman Filtering. By: Aaron . Dyreson. (aaron.dyreson@mavs.uta.edu). Supervising Professor: Dr. . Ioannis. . Schizas. (schizas@uta.edu). Introduction. Topic of Research: The performance of different distributed Kalman Filtering Algorithms in wireless sensor networks. COMS E6998-9. . F15. Administrivia. , Plan. Admin:. My office hours after class (CSB517). Plan:. Finalize . embeddings. Sublinear-time algorithms. Projects. Scriber?. 2. Embeddings of various metrics . Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. 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 ( Computaton. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box.: . A. A. A. A. Companion slides . for. Distributed Computing. Through . Combinatorial Topology. Maurice . Dr. . Barsha. . Mitra. CSIS . Dept. , BITS Pilani, Hyderabad Campus. Introduction. Course ID: SS ZG526, Title: Distributed Computing. allows for flexibly sharing resources (e.g., files and multimedia documents) stored across network-wide computers.