PPT-Computational and Communication Complexity in Massively Parallel

Computation Grigory Yaroslavtsev Indiana University Bloomington httpgrigoryus Cluster Computation a la BSP Input size n eg n billions of edges in a graph. April 26, 2013. Mark Braverman. Princeton University. Based on joint work with Ankit . Garg. , Denis . Pankratov. , and . Omri. Weinstein. Overview: information complexity. Information complexity . :: . 119. . submissions. Paris ‘09: . 113 . (deadline . after. STOC) . Prague '06: . 87. . Aarhus '03: . 65 . Florence '00: . 63. . 18 junk submissions. 34. papers accepted. Second highest ever? Paris ’09 had . Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. CUDA Lecture 1. Introduction to Massively Parallel Computing. A quiet revolution and potential buildup. Computation: TFLOPs . vs. . 100 GFLOPs. CPU in every PC – massive volume and potential impact. 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. Andris Ambainis (Latvia), . Martins Kokainis (Latvia), . Robin Kothari (MIT). communication complexity. Alice holds . x. X. , Bob holds . y. Y. .. D. cc. (f). - minimum amount of communication to compute . Parallel Computation. Complexity Measures for Parallel Computation. Problem parameters:. n. index of problem size. p. number of processors. Algorithm parameters:. t. p. running time on p processors. 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. June 1, 2013. Mark Braverman. Princeton University. a tutorial. Part I: Information theory. Information theory, in its modern format was introduced in the 1940s to study the problem of transmitting data over physical channels. . Today’s class. 1) Lecture. 2) . Blackbox. presentations. 3) Guest Lecture: Jonathan Mills. O. rganized . complexity. organized complexity. study of organization. whole is more than sum of parts. Systemhood. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand 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. Fall 2015. Lars Ailo Bongo (larsab@cs.uit.no). Course topics. Parallel programming. The parallelization process. Optimization of parallel programs. Performance analysis. Data-intensive computing. Parallel programs.