PPT-Scalable Parallelization of Skyline Computation for Multi-C

Presented by Dardan Xhymshiti Spring 2016 Paper Information Authors Publication ICDE 2015 Type Research Paper 2 Sean Chester Darius Sidlauskas Ira Assent Kenneth S . Kaushik. . Rajan. Abhishek. . Udupa. William Thies. Rigorous Software Engineering. Microsoft Research, India. Parallelization Reconsidered. Are there dependences between loop iterations?. No. Yes. DOALL Parallelism. of . Preference Queries . in . Data Streams : a Survey. M. . Kontaki. , A.N. Papadopoulos, . Y. . Manolopoulos. Data Engineering Lab. Department of Informatics. Aristotle . University. . of Thessaloniki. 12/01/2009. . Air France, Sophia . Antipolis. . . Solutions. and Services for . Accelerating. . your. Applications. . ProActive Parallel Suite . . Speed-up . applications by distributing them over a set of resources . With Skyline Bakery and Cafe. Brewed Awakenings--December. Java club specialties. Merry mint mochas. Hot spiced cider. Candy cane lattes. Breakfast club specialties. Gingerbread delight. Candied fruit pastries. k. representative . skyline items. Representative Skylines using Threshold-based Preference Distributions. Atish. Das . Sarma. , . Ashwin. . Lall. , . Danupon. . Nanongkai. , . Richard J. Lipton, Jim . School and Community Engagement. Creating a Legacy of Excellence. Getting involved at Skyline. There are a number of different opportunities to become involved at Skyline including:. Sports . Clubs. Foto. . Afrati. Paraschos. . Koutris. Dan . Suciu. Jeffrey Ullman. University of Washington. What is The Skyline?. A d-dimensional set R. A point . x . dominates. . x. ’ if . forall. k. :. . x. Date :. . 2012 . / . 04. . / . 12. 資訊碩一 . 10077034. 蔡勇儀 . @. . LAB603 . Outline. Introduction. Preliminaries. Method. Experimental result. Conclusions. Introduction. Image retrieval have more challenge than text retrieval.. via. Merge Semantics in STMs. Kaushik. . Ravichandran. (kaushikr@gatech.edu). Santosh. . Pande. (santosh@cc.gatech.edu) . College of Computing. Georgia Institute of Technology. Introduction. Connected Components Problem and Speculative Parallelization. Commutativity. Rule: Designing Scalable Software for Multicore Processors. Austin T. Clements, M. . Frans. . Kaashoek. , . Nickolai. . Zeldovich. , Robert T. Morris, and Eddie Kohler. MIT CSAIL and Harvard University. Processors. Presented by . Remzi. Can . Aksoy. *Some slides . are. . borrowed from a ‘Papers We Love’ . Presentation. EECS 582 – F16. 1. Outline. The . Scalable Commutativity Rule: . Whenever interface operations commute, they can be implemented in a way that scales. on Single-chip Shared-memory Multicores. Masab. Ahmad, . Kartik. . Lakhsminrarsimhan. , Omer Khan. University of Connecticut. Agenda. Motivation. Characterization Methodology. Characterization Results. Luis . Herranz. Arribas. Supervisor: Dr. José M. Martínez Sánchez. Video Processing and Understanding Lab. Universidad . Aut. ónoma. de Madrid. Outline. Introduction. Integrated. . summarization. ECE 751, Fall 2015. Peng . Liu. 1. Overview. What? JavaScript . Engine optimization. How? Light-weight . software speculation mechanism. 2. [1] Heine. , David, et al. Software and hardware for exploiting speculative parallelism with a multiprocessor. Computer Systems Laboratory, Stanford University, 1997.