PPT-Data Locality Optimizations

for NUMA Systems Zoltán Majó Thomas R Gross Department of Computer Science ETH Zurich Switzerland 1 A Library for Portable and Composable NUMA systems Key to good performance data locality. Bruce Worthington. Software Development Manager. Microsoft Corporation. Key Takeaways. Be a leader in advancing 64-bit computing. Adopt best practices and new tools. Let’s partner on new hardware directions. Optimization. Techniques. . Presented by . Preethi Rajaram. CSS 548 Introduction to Compilers . Professor Carol Zander. Fall 2012 . Why?. Processor Speed . -. increasing at a faster rate than the memory speed. A General Analytical Study. Yongkun. Li, . Patrick P. C. Lee. , John C. S. . Lui. , . Yinlong. Xu. The Chinese . University . of Hong . Kong. University of Science and Technology of China. . 1. SSD Storage. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Analysis of Data Locality. Optimality of Data Locality. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. Research at UNT. . . Krishna . Kavi. Professor. Director of NSF Industry/University Cooperative Center . for Net-Centric Software and Systems (Net-Centric IUCRC). Computer Science and Engineering. The University of North Texas. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Data Locality and Fairness. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. Data locality. Progress to Date. Irvine. Social isolation - all ages. Low level mental health and wellbeing - young people.. Musculoskeletal issues. North Coast. Social isolation - older people. Young people’s - stress & anxiety. Tyler Patton. Discussion:. Background. Sequential Optimizations. Parallelization of chess. Backgroun. d. : What is Chess?. Strategic 2 player game. 64 tiles. 16 pieces per player. Objective to capture the. Prof. O. . Nierstrasz. Lecture notes by Marcus . Denker. © Marcus . Denker. Optimization. Roadmap. Introduction. Optimizations in the Back-end. The Optimizer. SSA Optimizations. Advanced Optimizations. Arran 20. th. June 2016. Agenda. 6.30 – 6.45 . Introduction . and Background to Locality Partnerships .  . 6.45 – 7.15 . Data . – Information we have on Localities.  . 7.15 – 7.30 . UCI Annual Meeting. City of Hampton. May 25, 2017. David . S. Jarman. , P.E.. Transportation Project Management Supervisor. City of Virginia . Beach. Project Delivery Options Available. UCI . Member. Ross Tate. Michael Stepp. Sorin Lerner. University of California, San Diego. Optimizing by Hand. Original. for (i = 0; i < 50; i ). . for (j = 0; j < 50; j ). . img[i*50 j] = f(i, j);. T. Chen, T. Moreau, Z. Jiang, L. Zheng, S. Jiao, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. . Ceze. , C. . Guestrin. , and A. Krishnamurthy . Presentation by Grzegorz . Daniel Burk. What is a Predictive Model?. A GIS based model attempting to determine fossil locality potential. 1. Start with known fossil localities. 2. Compare their characteristics to other places.