PPT-Revisiting the Complexity of

Hardware Cache Coherence and Some Implications Rakesh Komuravelli Sarita Adve ChingTsun Chou University of Illinois UrbanaChampaign Intel denovoc sillinoisedu Motivation Todays shared memory systems are more complex than ever. How to celebrate it, leverage it, and NOT avoid it!. Are YOU sufficiently complex?. Simple = Best. FROM OCCAM’S . RAZOR. COMPLEXITY. Are YOU sufficiently complex?. Simple = Best. Simple but no. Simpler. China Theory Week, Aarhus. August 13, 2012. Today’s Goal:. To present new developments in a line of research dating back to 2002, presenting some unexpected connections between. Kolmogorov. Complexity (the theory of randomness), and. and . Hierarchy. Concept of Complexity. “whole is more than the sum of its parts”. Holism. new properties not found in subsystems. “mechanistic explanations of emergence rejected”. Weaker view of emergence. 1 A Global Significance of Revisiting Crouching Tiger, Hidden Dragon : Reinventing the Wuxia Tradition in the Sense of Global Sisterhood 何有田 大同大學&#x Revisiting the Central Dogma Kathleen B Hall, Ph.D. Dept Biochemistry & Molecular Biophysics kathleenhal@gmail.com Central Dogma: DNA RNA protein DNA RNA protein A revie Lecture 1: . Intro; Turing machines; . Class P and NP . . . Indian Institute of Science. About the course. Computational complexity attempts . to classify computational . problems. Lecture 1: . Intro; Turing machines; . Class P and NP . . . Indian Institute of Science. About the course. Computational complexity attempts . to classify computational . problems. Hardware Cache Coherence and Some Implications. Rakesh Komuravelli. Sarita. . Adve. , . Ching-Tsun. Chou. University of Illinois @ Urbana-Champaign, Intel. denovo@c. s.illinois.edu. Motivation. Today’s shared memory systems are more complex than ever. How Understanding Complexity Promotes Simplicity. Why is writing software hard?. ^. great. http://www.infoq.com/presentations/Simple-Made-Easy. Our duty as software architects is to understand and manage complexity. Theophilus Benson (tbenson@cs.wisc.edu). Aditya Akella (akella@cs.wisc.edu). David A Maltz (dmaltz@microsoft.com). Enterprise Networks. Intricate logical and physical topologies. Diverse network devices. Toniann. . Pitassi. University of Toronto. 2-Party Communication Complexity. [Yao]. 2-party communication: . each party has a dataset. . Goal . is to compute a function f(D. A. ,D. B. ). m. 1. m. 2. Fall . 2011. Sukumar Ghosh. What is an algorithm. . A finite set (or sequence) of . precise instructions . for performing a computation. . . . Example: Maxima finding. . . procedure . max. (. What is the best way to measure the time complexity of an algorithm?. - Best-case run time?. - Worst-case run time?. - Average run time?. Which should we try to optimize?. Best-Case Measures. How can we modify almost any algorithm to have a good best-case running time?. Lijie. Chen. MIT. Today’s Topic. Background. . What is Fine-Grained Complexity?. The Methodology of Fine-Grained Complexity. Frontier: Fine-Grained Hardness for Approximation Problems. The Connection.