A Practical Method for Estimating Performance Degradation on Multicore Processors, and its Application to HPC Workloads.
|
|
- Chad Simon
- 7 years ago
- Views:
Transcription
1 A Practical Method for Estimating Performance Degradation on Multicore Processors, and its Application to HPC Workloads Tyler Dwyer, Alexandra Fedorova, Sergey Blagodurov, Mark Roth, Fabien Gaud, Jian Pei 2012 Sameer Wadgaonkar Department of Computer & Information Sciences University of Delaware
2 Motivation Performance Degradation :- When Multiple programs are run on a modern multicore processor they compete for shared resources. Performance degradation is how much slower does each program run as compared to when run individually on the same system. Performance Degradation can be measured as high as 200%. This causes loss in time and power consumption.
3 Introduction In this paper the Authors have proposed: 1. A methodology for modeling performance degradation on multicore systems using machine learning. 2. Evaluating strengths and limitations of the resulting model. 3. Designing a confidence predictor that signals when the model is unable to produce an accurate estimate. 4. Demonstrating how the model can be applied to improve performance fidelity to save energy in HPC setting.
4 Model Testing Platforms: Two systems were used for building and testing the model, Intel and AMD. The models were built using exactly the same procedure on both the systems. The System parameters were as follows
5 Model Decision tree Learning was used to develop the model. The Nodes of the Decision tree are the attributes with their individual threshold values. Performance Degradation was calculated using the following formula The authors perform the above operation on all instances in the dataset. After the above procedure the authors had 340 attributes per core = 1360 attributes from the event counters.(intel) Weka, a machine learning tool was used for attribute selection. Correlation based feature subset attribute selection(cfssubset) was used within Weka. After attribute selection the number of attributes were reduced to 19 per core from the original 340 attributes per core.(intel)
6 Model List of Attributes selected after applying attribute selection for the Intel System.
7 Model The authors have used all modeling procedures available on Weka and compared each of them. After evaluation of all the models present in Weka the Authors choose REPTree as it yielded the highest accuracy. Regression tree mode was used instead of classification tree. The authors also used bagging to lower the error rate further.
8 Model Root of the Decision tree for Intel System. The number under the attribute is the value used for branching
9 Results Difference between the actual and predicted degradation for Best, Median and Worst Predicted co-schedules for each primary benchmark. The right most chart shows coscheduled when we apply the confidence predictor.
10 Results Baseline Cluster Scheduling Policies: Best-fit and Min-collocation. Best-fit allocates the process of the same job on all available cores on the node using additional nodes if needed, but if single job does not fill the cores, it fills them with processes from another job. Min-collocation attempts to schedule no more than one job per node, as long as there are unused nodes available. The Balanced Scheduler is based on the model described above. Job allocation across Nodes
11 Results Performance and energy consumption Experiment 1: Improved Performance Fidelity
12 Results Performance and energy consumption Experiment 2: Improved Power Efficiency
13 Conclusion The study was aimed to investigate the effectiveness of machine learning in modeling contention-induced performance degradation. The proposed model could be run on live workload without a prior knowledge of the applications or the need to run them in isolation. The model accurately estimates degradation within 16% of its true value. The confidence predictor will successfully estimate when the model is likely to produce an inaccurate estimate and reduce the maximum error.
14 Questions?
Optimizing Shared Resource Contention in HPC Clusters
Optimizing Shared Resource Contention in HPC Clusters Sergey Blagodurov Simon Fraser University Alexandra Fedorova Simon Fraser University Abstract Contention for shared resources in HPC clusters occurs
More informationPower Efficiency Metrics for the Top500. Shoaib Kamil and John Shalf CRD/NERSC Lawrence Berkeley National Lab
Power Efficiency Metrics for the Top500 Shoaib Kamil and John Shalf CRD/NERSC Lawrence Berkeley National Lab Power for Single Processors HPC Concurrency on the Rise Total # of Processors in Top15 350000
More informationDistributed forests for MapReduce-based machine learning
Distributed forests for MapReduce-based machine learning Ryoji Wakayama, Ryuei Murata, Akisato Kimura, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi Chubu University, Japan. NTT Communication
More informationA Holistic Model of the Energy-Efficiency of Hypervisors
A Holistic Model of the -Efficiency of Hypervisors in an HPC Environment Mateusz Guzek,Sebastien Varrette, Valentin Plugaru, Johnatan E. Pecero and Pascal Bouvry SnT & CSC, University of Luxembourg, Luxembourg
More informationStudying Auto Insurance Data
Studying Auto Insurance Data Ashutosh Nandeshwar February 23, 2010 1 Introduction To study auto insurance data using traditional and non-traditional tools, I downloaded a well-studied data from http://www.statsci.org/data/general/motorins.
More informationORACLE OPS CENTER: PROVISIONING AND PATCH AUTOMATION PACK
ORACLE OPS CENTER: PROVISIONING AND PATCH AUTOMATION PACK KEY FEATURES PROVISION FROM BARE- METAL TO PRODUCTION QUICKLY AND EFFICIENTLY Controlled discovery with active control of your hardware Automatically
More informationClassification On The Clouds Using MapReduce
Classification On The Clouds Using MapReduce Simão Martins Instituto Superior Técnico Lisbon, Portugal simao.martins@tecnico.ulisboa.pt Cláudia Antunes Instituto Superior Técnico Lisbon, Portugal claudia.antunes@tecnico.ulisboa.pt
More informationFACT: a Framework for Adaptive Contention-aware Thread migrations
FACT: a Framework for Adaptive Contention-aware Thread migrations Kishore Kumar Pusukuri Department of Computer Science and Engineering University of California, Riverside, CA 92507. kishore@cs.ucr.edu
More information!"!!"#$$%&'()*+$(,%!"#$%$&'()*""%(+,'-*&./#-$&'(-&(0*".$#-$1"(2&."3$'45"
!"!!"#$$%&'()*+$(,%!"#$%$&'()*""%(+,'-*&./#-$&'(-&(0*".$#-$1"(2&."3$'45"!"#"$%&#'()*+',$$-.&#',/"-0%.12'32./4'5,5'6/%&)$).2&'7./&)8'5,5'9/2%.%3%&8':")08';:
More informationData Mining: STATISTICA
Data Mining: STATISTICA Outline Prepare the data Classification and regression 1 Prepare the Data Statistica can read from Excel,.txt and many other types of files Compared with WEKA, Statistica is much
More informationA Property & Casualty Insurance Predictive Modeling Process in SAS
Paper AA-02-2015 A Property & Casualty Insurance Predictive Modeling Process in SAS 1.0 ABSTRACT Mei Najim, Sedgwick Claim Management Services, Chicago, Illinois Predictive analytics has been developing
More informationACCELERATING COMMERCIAL LINEAR DYNAMIC AND NONLINEAR IMPLICIT FEA SOFTWARE THROUGH HIGH- PERFORMANCE COMPUTING
ACCELERATING COMMERCIAL LINEAR DYNAMIC AND Vladimir Belsky Director of Solver Development* Luis Crivelli Director of Solver Development* Matt Dunbar Chief Architect* Mikhail Belyi Development Group Manager*
More informationTable of Contents. June 2010
June 2010 From: StatSoft Analytics White Papers To: Internal release Re: Performance comparison of STATISTICA Version 9 on multi-core 64-bit machines with current 64-bit releases of SAS (Version 9.2) and
More informationData Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification
More informationAddressing Shared Resource Contention in Multicore Processors via Scheduling
Addressing Shared Resource Contention in Multicore Processors via Scheduling Sergey Zhuravlev Sergey Blagodurov Alexandra Fedorova School of Computing Science, Simon Fraser University, Vancouver, Canada
More informationData Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.7 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Linear Regression Other Regression Models References Introduction Introduction Numerical prediction is
More informationIn-Situ Bitmaps Generation and Efficient Data Analysis based on Bitmaps. Yu Su, Yi Wang, Gagan Agrawal The Ohio State University
In-Situ Bitmaps Generation and Efficient Data Analysis based on Bitmaps Yu Su, Yi Wang, Gagan Agrawal The Ohio State University Motivation HPC Trends Huge performance gap CPU: extremely fast for generating
More informationEasily Identify Your Best Customers
IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do
More informationHigh Performance Computing for Operation Research
High Performance Computing for Operation Research IEF - Paris Sud University claude.tadonki@u-psud.fr INRIA-Alchemy seminar, Thursday March 17 Research topics Fundamental Aspects of Algorithms and Complexity
More informationKNIME TUTORIAL. Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it
KNIME TUTORIAL Anna Monreale KDD-Lab, University of Pisa Email: annam@di.unipi.it Outline Introduction on KNIME KNIME components Exercise: Market Basket Analysis Exercise: Customer Segmentation Exercise:
More informationDecision Trees from large Databases: SLIQ
Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values
More informationHow System Settings Impact PCIe SSD Performance
How System Settings Impact PCIe SSD Performance Suzanne Ferreira R&D Engineer Micron Technology, Inc. July, 2012 As solid state drives (SSDs) continue to gain ground in the enterprise server and storage
More informationData mining techniques: decision trees
Data mining techniques: decision trees 1/39 Agenda Rule systems Building rule systems vs rule systems Quick reference 2/39 1 Agenda Rule systems Building rule systems vs rule systems Quick reference 3/39
More informationAutomatic Workload Management in Clusters Managed by CloudStack
Automatic Workload Management in Clusters Managed by CloudStack Problem Statement In a cluster environment, we have a pool of server nodes with S running on them. Virtual Machines are launched in some
More informationApplied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets
Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification
More informationAbstract: Motivation: Description of proposal:
Efficient power utilization of a cluster using scheduler queues Kalyana Chadalvada, Shivaraj Nidoni, Toby Sebastian HPCC, Global Solutions Engineering Bangalore Development Centre, DELL Inc. {kalyana_chadalavada;shivaraj_nidoni;toby_sebastian}@dell.com
More informationOracle Enterprise Manager 13c Cloud Control
Oracle Enterprise Manager 13c Cloud Control ORACLE DIAGNOSTICS PACK FOR ORACLE DATABASE lace holder for now] Oracle Enterprise Manager is Oracle s integrated enterprise IT management product line, and
More informationVirtualization. Clothing the Wolf in Wool. Wednesday, April 17, 13
Virtualization Clothing the Wolf in Wool Virtual Machines Began in 1960s with IBM and MIT Project MAC Also called open shop operating systems Present user with the view of a bare machine Execute most instructions
More informationApplication of Predictive Analytics for Better Alignment of Business and IT
Application of Predictive Analytics for Better Alignment of Business and IT Boris Zibitsker, PhD bzibitsker@beznext.com July 25, 2014 Big Data Summit - Riga, Latvia About the Presenter Boris Zibitsker
More informationAzure Machine Learning, SQL Data Mining and R
Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:
More informationAnalysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News
Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News Sushilkumar Kalmegh Associate Professor, Department of Computer Science, Sant Gadge Baba Amravati
More informationKnowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes
Knowledge Discovery and Data Mining Lecture 19 - Bagging Tom Kelsey School of Computer Science University of St Andrews http://tom.host.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-19-B &
More informationModel Combination. 24 Novembre 2009
Model Combination 24 Novembre 2009 Datamining 1 2009-2010 Plan 1 Principles of model combination 2 Resampling methods Bagging Random Forests Boosting 3 Hybrid methods Stacking Generic algorithm for mulistrategy
More informationCI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore.
CI6227: Data Mining Lesson 11b: Ensemble Learning Sinno Jialin PAN Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Acknowledgements: slides are adapted from the lecture notes
More informationCharacterizing Task Usage Shapes in Google s Compute Clusters
Characterizing Task Usage Shapes in Google s Compute Clusters Qi Zhang 1, Joseph L. Hellerstein 2, Raouf Boutaba 1 1 University of Waterloo, 2 Google Inc. Introduction Cloud computing is becoming a key
More informationThe Data Center as a Grid Load Stabilizer
The Data Center as a Grid Load Stabilizer Hao Chen *, Michael C. Caramanis ** and Ayse K. Coskun * * Department of Electrical and Computer Engineering ** Division of Systems Engineering Boston University
More informationThe Impact of Memory Subsystem Resource Sharing on Datacenter Applications. Lingia Tang Jason Mars Neil Vachharajani Robert Hundt Mary Lou Soffa
The Impact of Memory Subsystem Resource Sharing on Datacenter Applications Lingia Tang Jason Mars Neil Vachharajani Robert Hundt Mary Lou Soffa Introduction Problem Recent studies into the effects of memory
More informationMethodology for predicting the energy consumption of SPMD application on virtualized environments *
Methodology for predicting the energy consumption of SPMD application on virtualized environments * Javier Balladini, Ronal Muresano +, Remo Suppi +, Dolores Rexachs + and Emilio Luque + * Computer Engineering
More informationData Mining Techniques for Prognosis in Pancreatic Cancer
Data Mining Techniques for Prognosis in Pancreatic Cancer by Stuart Floyd A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUE In partial fulfillment of the requirements for the Degree
More informationComparison of Data Mining Techniques used for Financial Data Analysis
Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract
More informationApplied Multivariate Analysis - Big data analytics
Applied Multivariate Analysis - Big data analytics Nathalie Villa-Vialaneix nathalie.villa@toulouse.inra.fr http://www.nathalievilla.org M1 in Economics and Economics and Statistics Toulouse School of
More informationAn Overview and Evaluation of Decision Tree Methodology
An Overview and Evaluation of Decision Tree Methodology ASA Quality and Productivity Conference Terri Moore Motorola Austin, TX terri.moore@motorola.com Carole Jesse Cargill, Inc. Wayzata, MN carole_jesse@cargill.com
More informationNoSQL Failover Characteristics: Aerospike, Cassandra, Couchbase, MongoDB
NoSQL Failover Characteristics: Aerospike, Cassandra, Couchbase, MongoDB Denis Nelubin, Director of Technology, Thumbtack Technology Ben Engber, CEO, Thumbtack Technology Overview Several weeks ago, we
More informationtesto dello schema Secondo livello Terzo livello Quarto livello Quinto livello
Extracting Knowledge from Biomedical Data through Logic Learning Machines and Rulex Marco Muselli Institute of Electronics, Computer and Telecommunication Engineering National Research Council of Italy,
More informationWindows Server 2008 R2 Hyper V. Public FAQ
Windows Server 2008 R2 Hyper V Public FAQ Contents New Functionality in Windows Server 2008 R2 Hyper V...3 Windows Server 2008 R2 Hyper V Questions...4 Clustering and Live Migration...5 Supported Guests...6
More informationTowards energy-aware scheduling in data centers using machine learning
Towards energy-aware scheduling in data centers using machine learning Josep Lluís Berral, Íñigo Goiri, Ramon Nou, Ferran Julià, Jordi Guitart, Ricard Gavaldà, and Jordi Torres Universitat Politècnica
More informationCOC131 Data Mining - Clustering
COC131 Data Mining - Clustering Martin D. Sykora m.d.sykora@lboro.ac.uk Tutorial 05, Friday 20th March 2009 1. Fire up Weka (Waikako Environment for Knowledge Analysis) software, launch the explorer window
More informationA Comparison of Variable Selection Techniques for Credit Scoring
1 A Comparison of Variable Selection Techniques for Credit Scoring K. Leung and F. Cheong and C. Cheong School of Business Information Technology, RMIT University, Melbourne, Victoria, Australia E-mail:
More informationAutonomous Resource Sharing for Multi-Threaded Workloads in Virtualized Servers
Autonomous Resource Sharing for Multi-Threaded Workloads in Virtualized Servers Can Hankendi* hankendi@bu.edu Ayse K. Coskun* acoskun@bu.edu Electrical and Computer Engineering Department Boston University
More informationClassification and Prediction
Classification and Prediction Slides for Data Mining: Concepts and Techniques Chapter 7 Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser
More informationPerformance Evaluation of NAS Parallel Benchmarks on Intel Xeon Phi
Performance Evaluation of NAS Parallel Benchmarks on Intel Xeon Phi ICPP 6 th International Workshop on Parallel Programming Models and Systems Software for High-End Computing October 1, 2013 Lyon, France
More informationFinal Project Report
CPSC545 by Introduction to Data Mining Prof. Martin Schultz & Prof. Mark Gerstein Student Name: Yu Kor Hugo Lam Student ID : 904907866 Due Date : May 7, 2007 Introduction Final Project Report Pseudogenes
More informationThe Gurobi Optimizer
The Gurobi Optimizer Gurobi History Gurobi Optimization, founded July 2008 Zonghao Gu, Ed Rothberg, Bob Bixby Started code development March 2008 Gurobi Version 1.0 released May 2009 History of rapid,
More informationNine Common Types of Data Mining Techniques Used in Predictive Analytics
1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better
More informationTHE HYBRID CART-LOGIT MODEL IN CLASSIFICATION AND DATA MINING. Dan Steinberg and N. Scott Cardell
THE HYBID CAT-LOGIT MODEL IN CLASSIFICATION AND DATA MINING Introduction Dan Steinberg and N. Scott Cardell Most data-mining projects involve classification problems assigning objects to classes whether
More informationPractical Data Science with Azure Machine Learning, SQL Data Mining, and R
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
More informationDell Virtualization Solution for Microsoft SQL Server 2012 using PowerEdge R820
Dell Virtualization Solution for Microsoft SQL Server 2012 using PowerEdge R820 This white paper discusses the SQL server workload consolidation capabilities of Dell PowerEdge R820 using Virtualization.
More informationData Mining III: Numeric Estimation
Data Mining III: Numeric Estimation Computer Science 105 Boston University David G. Sullivan, Ph.D. Review: Numeric Estimation Numeric estimation is like classification learning. it involves learning a
More informationPERFORMANCE ENHANCEMENTS IN TreeAge Pro 2014 R1.0
PERFORMANCE ENHANCEMENTS IN TreeAge Pro 2014 R1.0 15 th January 2014 Al Chrosny Director, Software Engineering TreeAge Software, Inc. achrosny@treeage.com Andrew Munzer Director, Training and Customer
More informationVirtualization. Types of Interfaces
Virtualization Virtualization: extend or replace an existing interface to mimic the behavior of another system. Introduced in 1970s: run legacy software on newer mainframe hardware Handle platform diversity
More informationPredicting Student Performance by Using Data Mining Methods for Classification
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 13, No 1 Sofia 2013 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.2478/cait-2013-0006 Predicting Student Performance
More informationWelcome to the IBM Education Assistant module for Tivoli Storage Manager version 6.2 Hyper-V backups. hyper_v_backups.ppt.
Welcome to the IBM Education Assistant module for Tivoli Storage Manager version 6.2 Hyper-V backups. Page 1 of 21 You are familiar with Tivoli Storage Manager version 5.5 or higher. Page 2 of 21 When
More informationData Mining Practical Machine Learning Tools and Techniques
Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea
More informationStatistical Process Control (SPC) Training Guide
Statistical Process Control (SPC) Training Guide Rev X05, 09/2013 What is data? Data is factual information (as measurements or statistics) used as a basic for reasoning, discussion or calculation. (Merriam-Webster
More informationUSTC Course for students entering Clemson F2013 Equivalent Clemson Course Counts for Clemson MS Core Area. CPSC 822 Case Study in Operating Systems
USTC Course for students entering Clemson F2013 Equivalent Clemson Course Counts for Clemson MS Core Area 398 / SE05117 Advanced Cover software lifecycle: waterfall model, V model, spiral model, RUP and
More informationClusters: Mainstream Technology for CAE
Clusters: Mainstream Technology for CAE Alanna Dwyer HPC Division, HP Linux and Clusters Sparked a Revolution in High Performance Computing! Supercomputing performance now affordable and accessible Linux
More informationTableau Server 7.0 scalability
Tableau Server 7.0 scalability February 2012 p2 Executive summary In January 2012, we performed scalability tests on Tableau Server to help our customers plan for large deployments. We tested three different
More informationBuilding an energy dashboard. Energy measurement and visualization in current HPC systems
Building an energy dashboard Energy measurement and visualization in current HPC systems Thomas Geenen 1/58 thomas.geenen@surfsara.nl SURFsara The Dutch national HPC center 2H 2014 > 1PFlop GPGPU accelerators
More informationScalable Data Analysis in R. Lee E. Edlefsen Chief Scientist UserR! 2011
Scalable Data Analysis in R Lee E. Edlefsen Chief Scientist UserR! 2011 1 Introduction Our ability to collect and store data has rapidly been outpacing our ability to analyze it We need scalable data analysis
More informationData quality in Accounting Information Systems
Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania
More informationRed Hat Enterprprise Linux - Renewals DETAILS SUPPORTED ARCHITECTURE
Red Hat Enterprprise Linux - Renewals PRODUCT CODE DESCRIPTION 1 Year DETAILS SUPPORTED ARCHITECTURE Red Hat Enterprise Linux Advanced Platform Red Hat Enterprise Linux Advanced Platform, (unlimited Red
More informationEnsemble Data Mining Methods
Ensemble Data Mining Methods Nikunj C. Oza, Ph.D., NASA Ames Research Center, USA INTRODUCTION Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods
More informationBest Practices. Server: Power Benchmark
Best Practices Server: Power Benchmark Rising global energy costs and an increased energy consumption of 2.5 percent in 2011 is driving a real need for combating server sprawl via increased capacity and
More informationMulti-Objective Job Placement in Clusters
Multi-Objective Job Placement in Clusters Sergey Blagodurov 2, Alexandra Fedorova 3, Evgeny Vinnik, Tyler Dwyer 3, and Fabien Hermenier 4 Simon Fraser University 2 Advanced Micro Devices, Inc. 3 The University
More informationLecture 10: Regression Trees
Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,
More informationTPCalc : a throughput calculator for computer architecture studies
TPCalc : a throughput calculator for computer architecture studies Pierre Michaud Stijn Eyerman Wouter Rogiest IRISA/INRIA Ghent University Ghent University pierre.michaud@inria.fr Stijn.Eyerman@elis.UGent.be
More informationBest Practices for Hadoop Data Analysis with Tableau
Best Practices for Hadoop Data Analysis with Tableau September 2013 2013 Hortonworks Inc. http:// Tableau 6.1.4 introduced the ability to visualize large, complex data stored in Apache Hadoop with Hortonworks
More informationA Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries
A Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries Aida Mustapha *1, Farhana M. Fadzil #2 * Faculty of Computer Science and Information Technology, Universiti Tun Hussein
More informationData Mining Classification: Decision Trees
Data Mining Classification: Decision Trees Classification Decision Trees: what they are and how they work Hunt s (TDIDT) algorithm How to select the best split How to handle Inconsistent data Continuous
More informationData Mining from A to Z: Better Insights, New Opportunities WHITE PAPER
Data Mining from A to Z: Better Insights, New Opportunities WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 How Do Predictive Analytics and Data Mining Work?.... 2 The Data Mining Process....
More informationBOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL
The Fifth International Conference on e-learning (elearning-2014), 22-23 September 2014, Belgrade, Serbia BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL SNJEŽANA MILINKOVIĆ University
More informationChapter 12 Bagging and Random Forests
Chapter 12 Bagging and Random Forests Xiaogang Su Department of Statistics and Actuarial Science University of Central Florida - 1 - Outline A brief introduction to the bootstrap Bagging: basic concepts
More informationAV-24 Advanced Analytics for Predictive Maintenance
Slide 1 AV-24 Advanced Analytics for Predictive Maintenance Big Data Meets Equipment Reliability and Maintenance Paul Sheremeto President & CEO Pattern Discovery Technologies Inc. social.invensys.com @InvensysOpsMgmt
More informationWeb Document Clustering
Web Document Clustering Lab Project based on the MDL clustering suite http://www.cs.ccsu.edu/~markov/mdlclustering/ Zdravko Markov Computer Science Department Central Connecticut State University New Britain,
More informationCSC 177 Fall 2014 Team Project Final Report
CSC 177 Fall 2014 Team Project Final Report Project Title, Data Mining on Farmers Market Data Instructor: Dr. Meiliu Lu Team Members: Yogesh Isawe Kalindi Mehta Aditi Kulkarni CSc 177 DM Project Cover
More informationA Property and Casualty Insurance Predictive Modeling Process in SAS
Paper 11422-2016 A Property and Casualty Insurance Predictive Modeling Process in SAS Mei Najim, Sedgwick Claim Management Services ABSTRACT Predictive analytics is an area that has been developing rapidly
More informationWhite Paper. Redefine Your Analytics Journey With Self-Service Data Discovery and Interactive Predictive Analytics
White Paper Redefine Your Analytics Journey With Self-Service Data Discovery and Interactive Predictive Analytics Contents Self-service data discovery and interactive predictive analytics... 1 What does
More informationClassification of Bad Accounts in Credit Card Industry
Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition
More informationComparison of Windows IaaS Environments
Comparison of Windows IaaS Environments Comparison of Amazon Web Services, Expedient, Microsoft, and Rackspace Public Clouds January 5, 215 TABLE OF CONTENTS Executive Summary 2 vcpu Performance Summary
More informationData Mining. Nonlinear Classification
Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15
More informationSERVER CLUSTERING TECHNOLOGY & CONCEPT
SERVER CLUSTERING TECHNOLOGY & CONCEPT M00383937, Computer Network, Middlesex University, E mail: vaibhav.mathur2007@gmail.com Abstract Server Cluster is one of the clustering technologies; it is use for
More informationMONITORING power consumption of a microprocessor
IEEE TRANSACTIONS ON CIRCUIT AND SYSTEMS-II, VOL. X, NO. Y, JANUARY XXXX 1 A Study on the use of Performance Counters to Estimate Power in Microprocessors Rance Rodrigues, Member, IEEE, Arunachalam Annamalai,
More informationEnsemble Learning of Colorectal Cancer Survival Rates
Ensemble Learning of Colorectal Cancer Survival Rates Chris Roadknight School of Computing Science University of Nottingham Malaysia Campus Malaysia Chris.roadknight@nottingham.edu.my Uwe Aickelin School
More informationSocial Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
More informationClassification/Decision Trees (II)
Classification/Decision Trees (II) Department of Statistics The Pennsylvania State University Email: jiali@stat.psu.edu Right Sized Trees Let the expected misclassification rate of a tree T be R (T ).
More informationUsing multiple models: Bagging, Boosting, Ensembles, Forests
Using multiple models: Bagging, Boosting, Ensembles, Forests Bagging Combining predictions from multiple models Different models obtained from bootstrap samples of training data Average predictions or
More informationCOMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Big Data by the numbers
COMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Instructor: (jpineau@cs.mcgill.ca) TAs: Pierre-Luc Bacon (pbacon@cs.mcgill.ca) Ryan Lowe (ryan.lowe@mail.mcgill.ca)
More informationMarketing Strategies for Retail Customers Based on Predictive Behavior Models
Marketing Strategies for Retail Customers Based on Predictive Behavior Models Glenn Hofmann HSBC Salford Systems Data Mining 2005 New York, March 28 30 0 Objectives Inform about effective approach to direct
More informationCONTINUOUSLY IMPROVE TO OBTAIN THE ISO 50001
CONTINUOUSLY IMPROVE TO OBTAIN THE ISO 50001 WHAT IS THE ISO 50001? The goal of the standard is to create an energy management system within an organization that leads to a reduction in greenhouse gas
More informationInternational Journal of Computer Trends and Technology (IJCTT) volume 4 Issue 8 August 2013
A Short-Term Traffic Prediction On A Distributed Network Using Multiple Regression Equation Ms.Sharmi.S 1 Research Scholar, MS University,Thirunelvelli Dr.M.Punithavalli Director, SREC,Coimbatore. Abstract:
More information