PPT-Thread Criticality Predictors

for Dynamic Performance Power and Resource Management in Chip Multiprocessors Abhishek Bhattacharjee Margaret Martonosi Princeton University Why Thread Criticality Prediction DCache Miss I. Kaushik . Banerjee. . and John . M. . Scaglione. Oak Ridge National Laboratory. Contact: . banerjeek@. ornl.gov. International Conference on Nuclear Criticality Safety. September 13-17, 2015. Charlotte, NC. Organizations. American National Standards Institute (ANSI). Promulgation of standards. Nation’s point guard for international standardization (ISO). ANS. American Nuclear Society. Joint numbers with ANSI. NCS . Engineer Hands-On Subcritical and Critical Experiments Training and Education Course. Sedat Goluoglu. Calvin M. Hopper. Project Coordinators. EFCOG Safety Analysis Workshop 2012. Santa Fe, NM. May 4-10, 2012. Scope: Regression with a single dependent variable Y and many correlated predictors. 1. Some Differences Between PLS-R and CCR (K<P). Invariant to . Predictor Scaling?. Components Correlated?.  . PARTHASARATHI G, HARUGERI A, JOSEPH J, RAMESH M, GUIDO S..  . Presenting Author. G Parthasarathi. JSS University, INDIA. 1. Introduction. Several factors contribute to greater propensity . of ADRs . Abhilash Thekkilakattil. , Alan Burns, Radu Dobrin and Sasikumar Punnekkat. Motivation and Contribution. State of the art of mixed criticality scheduling . mainly . focuses . on. WCET overruns. WCET overruns are . Menu Drop-Down Choices. Logits. :. Modeling data. Logistic regression. Response variable—highlight your choice, . dep. . var. Quantitative—those are the continuous variable predictors, if you have any. Kjell Max Methodologies to Reproducible Modeling. Types of Modeling. What . are the Potential Answer(s)?. What . are the Question(s)?. 7 Step “Lean” process for Multivariate Model Development. 1. Models and Methods. Haohan Li. October 08, 2010. Motivation. DO 178-B Criticality Levels. A: Catastrophic. B: Hazardous. C: Major. D: Minor. E: No Effect. WCET Estimations Varies by Levels. Motivation. Glidescope. Video Laryngoscopy. 1. . Michael Aziz, MD. Ansgar Brambrink, MD. Department of Anesthesiology & Perioperative Medicine. Oregon Health & Science University. Portland, OR. Introduction:. c. riticality of stresses. , . threats and contributing . f. actors. Phase II. Systemic risk and vulnerability . analysis. Step 12. © Pierre . Ibisch. 2013. Credits and conditions of use. 12. Analysis of the future criticality of stresses, threats and contributing factors. Data Mining for Business Analytics. Shmueli. , Patel & Bruce. Discriminant Analysis: Background. A classical statistical technique. Used for classification long before data mining. Classifying organisms into species. Copyright © Cengage Learning. All rights reserved. 13 Nonlinear and Multiple Regression Copyright © Cengage Learning. All rights reserved. 13.4 Multiple Regression Analysis Multiple Regression Analysis February 3, . 2011. Overview. Multivariate data simulation. Added . variable plots (review). Partial correlation. The problem of . collinearity. Regression diagnostics:. Review of assumption checking.