PPT-Accelerated Path-Based Timing Analysis with MapReduce

Author : trish-goza | Published Date : 2016-03-04

Tsung Wei Huang and Martin D F Wong Department of Electrical and Computer Engineering ECE University of Illinois at UrbanaChampaign UIUC IL USA 2015 ACM International

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Accelerated Path-Based Timing Analysis with MapReduce: Transcript

Tsung Wei Huang and Martin D F Wong Department of Electrical and Computer Engineering ECE University of Illinois at UrbanaChampaign UIUC IL USA 2015 ACM International Symposium on Physical Design ISPD. Closure. Page . 2. Welcome. This module will help you understand how your synthesis tool, the ISE software, HDL coding style, and other factors that affect your ability to meet your system timing objectives. Objectives. After completing this module you will be able to…. Apply global timing constraints to a simple synchronous design. Use the Xilinx Constraints Editor to specify global timing constraints. Subramanyam. Sripada. Synopsys . Inc. 3/13/2015. Constraint Analysis/Debug/Management. Budgeting Issues. Functional. Scan. Test. Low_Power. More Modes. More Files/. More complex. constraints. IP Blocks. , Collective Communication, and Services. Oral Exam, . Bingjing. Zhang. Outline. MapReduce. MapReduce. Frameworks. Iterative . MapReduce. Frameworks. Frameworks Based on . MapReduce. and Alternatives. Parallel Computing. MapReduce. Examples. Parallel Efficiency. Assignment. Parallel Computing. Parallel efficiency with . p. processors. Traditional parallel computing:. focus on compute intensive tasks. Objectives. After completing this module, you will be able to:. Describe a flow for obtaining timing closure. Interpret a timing report and determine the cause of timing errors. Apply Timing Analyzer report options to create customized timing reports. Qiuyang Wu. 2015.3.13. Outline. This talk answers the following questions regarding timing constraints:. What are the key scalability challenges?. What are the available solutions?. Design Complexity Trends. Accelerated Reading. Accelerated Reader . combines two key elements of baseline assessment and personalised reading practice to promote reading for pleasure and encourage pupils to use reading to learn.. By . Yufei. Tao, . Wenqing. . Lin, . Xiaokui. . Xia. Edited by Tuval Rotem. First topic:. What is MapReduce. But first…. Regular day at work. You’re doing your regular work while suddenly your boss, . Why?. In order for year 11 pupils to access their GCSE exams effectively to achieve pupils need to have an average reading age of at least 15 years. 25% of year 11 pupils in the UK have a reading age less than 11 years old.. Yasin N. Silva and Jason Reed. Arizona State University. 1. This work is licensed under a Creative Commons Attribution-. NonCommercial. -. ShareAlike. 4.0 International License. See http://creativecommons.org/licenses/by-nc-sa/4.0/ for details.. Karen Taylor & . Evangelia. . Antonakos. Bronx Community College. Dept. of Math & Computer Science. Innovative Practices in Developmental Mathematics. LaGCC. , CUNY. April 15, 2016. . Supported by a CUNY course redesign grant awarded to Karen Taylor & Suzanne . Source. MapReduce. : Simplified Data Processing in Large Clusters. . Jefferey. Dean and Sanjay . Ghemawat. . OSDI 2004. Example Scenario. 3. Genome data from roughly . one million users. 125 MB of data per user. MapReduce. Architecture. MapReduce. Internals. MapReduce. Examples. JobTracker. Interface. MapReduce. : A Real World Analogy. Coins Deposit. ?. MapReduce. : A Real World Analogy. Coins Deposit. Coins Counting Machine.

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