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Peter CS301 Kemper R 104A, phone 221-3462, email:kemper@cs.wm.edu Grader: Fengyuan Xu, R 101C, email: fxu@wm.edu Today: Profiling and Performance Tuning 1 Overview this is the plan Object oriented programming & Java Object oriented design process Interface Type and Polymorphism Patterns and GUI Programming Inheritance and Abstract Classes Java Object Model and Reflection Frameworks More Patterns Multithreading Performance Issues Tools: -Eclipse -Junit -Javadoc -Ant -PMD -Findbugs . 2 This week s topic what is it good for Functional correctness is necessary for user satisfaction but not sufficient, there are more requirements: Among those: Performance This lecture describes techniques & tools to apply late in the development cycle: Profiling Monitoring Performance tuning Profiling tools exist for most programming languages, in the Java arena we consider Sun Hprof Eclipse tptp The biggest difference CHUD Shark between time and space is that you can't reuse time. -- Merrick Furst 3 Some references Jack Shirazi, Java Performance Tuning, O Reilly Collection of tips for performance tuning in Java http://www.javaperformancetuning.com/tips/rawtips.shtml Sun White paper on performance tuning http://java.sun.com/javase/technologies/performance.jsp http://java.sun.com/performance/reference/whitepapers/tuning.html Hprof profiler http://java.sun.com/developer/technicalArticles/Programming/HPROF.html The -Xprof profiler (HotSpot Profiler) http://www.javaperformancetuning.com/news/qotm037.shtml Mac: Shark http://developer.apple.com/tools/shark_optimize.html Eclipse: TPTP http://www.eclipse.org/tptp/ 4 Scenario Software development Design + Implementation + Test + Given you have an executable implementation: Does it perform? Memory usage CPU time usage Your personal, intuitive estimation for how much time which methods take is often wrong ! But why? What influences the performance of my code? 5 Issues: Memory allocation and release, Garbage collection Operations C: Java: Allocation: malloc Release: free Allocation: new Release: -- done by garbage collection Why costly: Datastructures & methods for management at two levels Requires runtime memory management operations Requires OS memory management operations Allocation: determine demand, reserve space Release: Identify unused memory locations merge with other adjacent free locations to larger parts Instantiation of objects requires initialization May execute a sequence of constructor methods (inheritance) Example: Traviando Jcomponent instantiation is costly Influence Configuration: how much space available for heap? Too little for application: GC takes a lot of time Too much for architecture: OS virtual memory thrashing possible if application really uses space + access pattern lacks locality 6 More Issues with Performance Costs of method calls System calls Requires mode switch, may cause blocking User code Push/pop operation on stack Push requires to allocate and initialize local variables + parameters Example: Sorted linked list, elements sorted wrt a given total order has methods contains(), insert(), get(), Variant 1: recursive Variant 2: iterative 7 More Issues with Performance Costs of binding & loading Static vs dynamic & runtime binding of libraries Compiled vs interpreted code Interpreter: computes statement, reads statement, interpretes statement: maps to machine language & executes Compiled: maps code to machine language links code with libraries loads executable code (runtime linking may apply) runs code as given (fixed) In between: Java Compiled to byte code Executed in Hot Spot JVM with just in time compilation, adaptive optimization of code for frequently executed pieces of code 8 More Issues with Performance Hardware architecture CPU, buses, memory hierarchy (L1, L2, main memory, disk) Effectively available CPU time Memory management (virtual memory, page faults) Process management (scheduling, process/mode/thread switches) Memory allocation and release, Garbage collection Costs for methods calls Thread scheduling Configuration: Configure SW/HW environment to make application fit Operating System Runtime environment What can we influence? Programming Recode performance-critical parts of program How to figure out what goes on in program execution? Monitor Profile CPU time, memory requirements, code coverage Instrument and measure execution time 9 Measuring Program Performance: Profiling Basic idea: Execute program Measure time and memory usage for each method call Create statistics, reports Heisenberg s uncertainty principle: Measuring with same HW has impact on results, Too coarse measurement -> imprecise results Too fine measurement -> imprecise results Challenges: Seletion of critical/typical/interesting/representative program execution to achieve data that is meaningful create profile (amount of data proportional to #methods) By sampling: check program state, exec stacks periodically By code instrumentation: add code that reports to statistics Profiling-Techniques : create trace (amount of data proportional to execution time) By event logging 10 Profiler Profiles entire system Kernel, drivers, applications Simple: tells you in which methods code spends time Advanced I: low-level, drill down Cache misses, virtual memory activity, memory allocations, function calls, instruction dependency stalls Advanced II: distributed Multiple processes Remote processes Networking applications Profiles 11 General guidelines for finding good candidates for optimization Operation should be time-critical, perceptibly slow, annoying Operation should be relevant, often, frequently in use rarely used, but critical (relevant for majority of uses) (relevant for important uses) Operation should show up as a hot spot in time profile. obvious key for improvement 12 Different types of optimizations Low-level optimizations Focus on single segment of code Aim at better match of hardware/software systems E.g. vector, cache hint instructions May trade Platform dependency Clarity, Simplicity of code Maintainability For Performance High-level optimizations Algorithmic or other architectural changes to program E.g. choice of data structure, replace calls to computationally expensive functions with cache / lookup table 13 When to apply profiling? Ahead of any optimizations! Premature optimization is the root of all evil. Donald Knuth Improving code that you believe is performance-critical but that in reality costs < 1% of application s time gives potential benefits in same range Profiling performance identifies critical sections of code IF the input is appropriately chosen I.e. wrt to real usage 14 Example Profiler: Sun s hprof For CPU and heap analysis Dynamically linked native library, uses JVM TI (tool interface) Writes to socket or file descriptor, ascii or binary formats Simple example collected by sampling Command used: javac -J-agentlib:hprof=cpu=samples Hello.java CPU SAMPLES rank self 1 53.17% 2 17.46% 3 5.56% 4 3.97% 5 2.38% 6 1.59% 7 1.59% 8 1.59% 9 0.79% 10 0.79% BEGIN (total = accum count 53.17% 67 70.63% 22 76.19% 7 80.16% 5 82.54% 3 84.13% 2 85.71% 2 87.30% 2 88.10% 1 88.89% 1 126) Fri Oct 22 12:12:14 2004 trace method 300027 java.util.zip.ZipFile.getEntry 300135 java.util.zip.ZipFile.getNextEntry 300111 java.lang.ClassLoader.defineClass2 300140 java.io.UnixFileSystem.list 300149 java.lang.Shutdown.halt0 300136 java.util.zip.ZipEntry.initFields 300138 java.lang.String.substring 300026 java.util.zip.ZipFile.open 300118 com.sun.tools.javac.code.Type$ErrorType.<init> 300134 java.util.zip.ZipFile.ensureOpen 2nd example collected by Byte code insertion Command used: javac -J-agentlib:hprof=cpu=times Hello.java CPU TIME (ms) BEGIN (total = 103099259) Fri Oct 22 12:21:23 2004 rank self accum count trace method 1 5.28% 5.28% 1 308128 com.sun.tools.javac.Main.compile 2 5.16% 10.43% 1 308127 com.sun.tools.javac.main.Main.compile 3 5.15% 15.58% 1 308126 com.sun.tools.javac.main.Main.compile 4 4.07% 19.66% 1 308060 com.sun.tools.javac.main.JavaCompiler.compile 5 3.90% 23.56% 1 306652 com.sun.tools.javac.comp.Enter.main 6 3.90% 27.46% 1 306651 com.sun.tools.javac.comp.Enter.complete 15 Example profiler: Shark (Mac OS) Creates profile of execution behavior Periodically interrupts each processor Samples running process, thread, instruction address, function call stack, contextual information Overhead for measurement: low Takes place in kernel and based on hardware interrupts Occasionally high: If call stack recording enabled and virtual memory fault incurs while saving the call stack Results are statistical Sampling During time interval (timer sampling) Triggered by event (event sampling) E.g. cache misses, instruction stalls Performs symbol lookup to relate to source code 16 Shark example (from Profiling tutorial) Example application: flurry screensaver (http://homepage.mac.com/calumr/) slightly modified for benchmarking First step: Define performance metric Justify its relevance Measurement should be precise and consistent. Here: Render 1000 frames We can measure length of time precisely & repeatedly 17 Measure base line with time profile Observe: Clear Hot spot sqrt() Think: Reasonable or not? Decision: Double precision not required 18 In certain cases: Tool makes suggestions Improvement: Use approximate algorithm for sqrt() Implementation already available! Numerical recipes in C Good source of algorithms 19 Profile improved version Check if sqrt() hot spot is gone Yes Time drops to 11.5 s, performance improves 1.9x Shows good candidate for low-level optimization Use Code Browser to relate back to source code inside UpdateSmoke 20 Code Browsers Useful to track root cause inside method Automated guidance as extra bonus Loop unrolling + vectorize floating point executions (HW depend!) 21 and so on After some more steps that one may debate as a tradeoff Architecture / platform dependency increased Maintainability, simplicity of code (locally) reduced Performance substantially increased The overall results are significant 22 So far Shark tutorial illustrates nice case Low level optimization yields improvement by 1 order of mag! For particular test case (productioncell1kevents.xml) Characteristics: Let s check Traviando s basic statistics package #actions > 200, #variables > 200, #processes < 10 Length of sequence: 1000 events Measure: Load Trace Perform Trace reduction Calls for Basic Statistics without user request 23 A Shark Attack on Traviando Let s check Traviando s basic statistics package For particular test case (productioncell1kevents.xml) Two features applied BasicStatistics precomputed by separate thread Trace reduction method Indicate 18.5% of time in State.equals(), caused by Trace reduction <- Focus 5% of time in basicStatistics.getVariable(), caused by statistics No automated suggestions for improvement Asks for high level optimization: State.equals() Switch data structure from list to hash table or array basicStatistics.getVariable() Switch lookup from linear search in unsorted list to hashing Important: Profiler gives exact code fragment that asks for improvement 24 Eclipse Test and Performance Tools Platform (TPTP) Provides a selection of tools, among those a profiler that applies to Eclipse plug-ins Java Applications Simple, local applications Distributed, running on multiple hosts and different platforms for identifying and isolating performance problems such as performance bottlenecks, object leaks, system resource limits. The following slides are based on a web tutorial Java Application Profiling using TPTP by Valentina Popescu, IBM 25 Running an example program with profiling Profile the application by using popup-menu Profile As > Java Application on the example, Or same way as run, debug, Subsequently fix arguments, class path, in launch wizzard 26 Setting profiling filters Specify what the profiler should measure 27 Select Classes to be Profiled Java Profiling, Edit Options, Use existing filters, Add new filters, Exclude/Include Classes 28 Run application Run application, Switch to Perspective to Profiling and Logging, Possible: interact with application. Pause, resume monitoring, run garbage collection collect object references terminate the application. 29 Identify Hot Spots in Execution Statistics view Select process in Profiling Monitor view, select Open with > Execution Statistics pop-up action Views shows methods ordered by cumulative exec time (includes time for invocation of nested methods) Pick methods that take more time than expected, drill down to details: Open Method Invocation Details view on the createParser() method 30 Summary Performance Tuning Make a plan, determine goal, metric, workload Note: results/bottlenecks are workload dependent! Perform measurements, document results Identify bottlenecks Tune system configuration (HW, SW, OS, Runtime environment) Determine load driver ( Heavy Hitters ) for bottleneck resources (80-20 rule) Profile load driver, identify Hot Spots (80-20 rule) Identify solution strategies Automated optimization Software refactoring: High-level & Low-level optimization Select, implement and profile selected solution Iterate if necessary 31
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v20.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Exponential D...
v17.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Discrete Time...
hw1.pdf
Path: Wisconsin Milwaukee >> CS >> 780 Fall, 2009
Description: CS 780: Discrete State Models Homework #1 Due Feb 1, 2008 The goals of this exercise are for you to become familiar with the mechanics of modeling using Mbius and analysis of systems using Fault Trees. Completing this exercise will assist you in comp...
V_CCS.pdf
Path: Wisconsin Milwaukee >> CS >> 780 Fall, 2009
Description: CS780 Discrete-State Models Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Today: Milners Calculus of Communicating Systems Strong & Weak Bisimulation Observational Congruence Quick Reference: Rob...
v7.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Moment Genera...
v4.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Yunlian Jiang R 101B, email: jiang@cs.wm.edu Grader: Today: More Distributions Quick Reference: Ross, C...
hw6.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: Computer Science 616 - Stochastic Models in Computer Science Fall 2007, Homework 6, due Nov 28 at noon 1. Let the transition probability matrix of a two-state Markov chain be given by P = with 0 < p < 1. Prove that p 1p 1p p , P (n) = 1 2 ...
v3.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Yunlian Jiang R 101B, email: jiang@cs.wm.edu Grader: Today: Random Variables Quick Reference: Ross, Ch....
v1.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS626 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Yunlian Jiang R 101B, email: jiang@cs.wm.edu Grader: Today: Overview & Introduction Quick Reference: Ros...
v14a.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Markov Chains...
v9.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Conditional P...
v16.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Markov Chains...
v12.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Conditional P...
v8.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Limit Theorem...
V3aJavadoc.pdf
Path: Wisconsin Milwaukee >> CS >> 301 Fall, 2009
Description: CS301 Peter Kemper, R 104A, phone 221-3462, email:kemper@cs.wm.edu Grader: Fengyuan Xu, R 101A, email: fxu@cs.wm.edu Today: Notes on JavaDoc Quick Reference: http:/java.sun.com/j2se/javadoc/writingdoccomments/index.html http:/java.sun.com/javase/6/d...
V12.pdf
Path: Wisconsin Milwaukee >> CS >> 301 Fall, 2009
Description: CS301 Peter Kemper, R 104A, phone 221-3462, email:kemper@cs.wm.edu Grader: Fengyuan Xu, R 11A, email: fxu@cs.wm.edu Today: Static code analysis TPTP, PMD, findbugs 1 This weeks topic what is it good for Static code analyzer are helpful to detect ...
v6.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Yunlian Jiang R 101B, email: jiang@cs.wm.edu Grader: Office hours: Tue,Thu 2-3 pm Today: Jointly Distrib...
hw1.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: Computer Science 616 - Stochastic Models in Computer Science Fall 2007, Homework 1, due Sept 7 at noon 1. We know that the denition of independence for n events E = {E1 , E2 , . . . , En }, F, F E, F = {F1 , F2 , . . . , Fm }, P (F1 F2 . . . Fm ...
hw3.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: Computer Science 616 - Stochastic Models in Computer Science Fall 2007, Homework 3, due Sept 24 at noon 1. Chapter 2, Exercises 17, 18, 19, 20. 17. Suppose that an experiment can result in one of r possible outcomes, the ith outcome having probabili...
v10.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Conditional P...
hw4.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: Computer Science 616 - Stochastic Models in Computer Science Fall 2007, Homework 4, due Oct 3 at noon 1. This homework is to prepare you for stochastic modeling and analysis with Mobius. (a) Download Mobius from http:/www.mobius.uiuc.edu/download.ht...
hw2.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: Computer Science 616 - Stochastic Models in Computer Science Fall 2007, Homework 2, due Sept 17 at noon 1. (Section 2, Exercise 12) On an exam with ve multiple-choice questions, each with three possible answers, what is the probability of getting fo...
l4a.pdf
Path: Wisconsin Milwaukee >> CS >> 654 Fall, 2009
Description: Dependability Dependability is the ability of a system to deliver a specified service. System service is classified as proper if it is delivered as specified; otherwise it is improper. System failure is a transition from proper to improper servic...
v13.pdf
Path: Wisconsin Milwaukee >> CS >> 616 Fall, 2009
Description: CS616 Stochastic Models in Computer Science Instructor: Peter Kemper R 006, phone 221-3462, email:kemper@cs.wm.edu Office hours: Mon,Wed 3-5 pm Grader: Yunlian Jiang, R 101B, email: jiang@cs.wm.edu Office hours: Tue,Thu 2-3 pm Today: Applications ...
04Scope.pdf
Path: Wisconsin Milwaukee >> CSC >> 652 Fall, 2009
Description: Topics of This Class ! ! ! Related issues on local value numbering Scope of optimization Value numbering over extended basic blocks CSC652 Scope of Optimization Xipeng Shen 1/25/2007 1 Review: Local Value Numbering An example Original Code a!x+y ...
26codeShape_I_.pdf
Path: Wisconsin Milwaukee >> CSC >> 442 Fall, 2009
Description: Introduction ! Translate program into 3-address intermediate representation A preparation for some code optimization and backend code generation Major problems to address: \" Translation of Statements (I) Xipeng Shen 10/29/2007 ! ! How to transla...
CudaOptimizor.pdf
Path: Wisconsin Milwaukee >> CSC >> 652 Fall, 2009
Description: Goal ! A tool to automatically explore optimization space \" \" (Front-end) Accept cuda programs with annotations (Middle-end) Produce transformed cuda programs # Source-to-source CSC652 GPU Optimizor Project Xipeng Shen 04/02/08 optimizor \" (Fee...
02introLearning.pdf
Path: Wisconsin Milwaukee >> CSC >> 420 Fall, 2009
Description: CSCI420/520 Introduction to Statistical Learning Xipeng Shen 9/3/2006 Outline Review of last class Learning concept Designing a learner to play checkers Classification examples 2 Review of last class What is Artificial Intelligence? A st...
03MatrixAndProb.pdf
Path: Wisconsin Milwaukee >> CSC >> 420 Fall, 2009
Description: CSCI420/520 Review on Matrix and Probability Theory Xipeng Shen 9/6/06 Outline Quiz on matrix differentiation assignment Review of last class with answers to the quiz Matrix operations Probability theory 1 Design Choices Determine Type of Tra...
21EM.pdf
Path: Wisconsin Milwaukee >> CSC >> 420 Fall, 2009
Description: CSCI420/520 Expectation-Maximization (EM) Algorithm Xipeng Shen 10/20/06 Equivalence between ML and least squares hML 2 = arg max P(D | h) arg min (yi yi ) hH hH i =1 N Assumptions Additive Gaussian noise y = f (x) + Mutually independent ...
section4.2.pdf
Path: Wisconsin Milwaukee >> CS >> 526 Fall, 2009
Description: Discrete-Data Histograms Discrete-Event Simulation: A First Course Section 4.2: Discrete-Data Histograms Section 4.2: Discrete-Data Histograms Discrete-Event Simulation c 2006 Pearson Ed., Inc. 0-13-142917-5 Discrete-Data Histograms Section 4....
section7.1.pdf
Path: Wisconsin Milwaukee >> CS >> 526 Fall, 2009
Description: Continuous Random Variables Discrete-Event Simulation: A First Course Section 7.1: Continuous Random Variables Section 7.1: Continuous Random Variables Discrete-Event Simulation c 2006 Pearson Ed., Inc. 0-13-142917-5 Continuous Random Variables...
hw3.pdf
Path: Wisconsin Milwaukee >> CS >> 424 Fall, 2009
Description: CSci424/524 Homework 3 Due: 02/26/2008 You are asked to add the datapath parts and control signals needed to implement the addiu instruction in the multi-cycle datapath and control. Instruction addiu adds an immediate unsigned value. For example:...
section7.2.pdf
Path: Wisconsin Milwaukee >> CS >> 526 Fall, 2009
Description: Generating Continuous Random Variates Discrete-Event Simulation: A First Course Section 7.2: Generating Continuous Random Variates Section 7.2: Generating Continuous Random Variates Discrete-Event Simulation c 2006 Pearson Ed., Inc. 0-13-142917-...
examples.pdf
Path: Wisconsin Milwaukee >> CS >> 526 Fall, 2009
Description: CSci 426/526 Simulation Fall 2004 Examples To help you get a sense of my expectations, enclosed are (partial) solutions to three typical exercises. These three exercises illustrate the range of solution types you will be expected to produce. exerc...
section8.1.pdf
Path: Wisconsin Milwaukee >> CS >> 526 Fall, 2009
Description: Interval Estimation Discrete-Event Simulation: A First Course Section 8.1: Interval Estimation Section 8.1: Interval Estimation Discrete-Event Simulation c 2006 Pearson Ed., Inc. 0-13-142917-5 Interval Estimation Section 8.1: Interval Estimati...
07-the_structure_and_value_of_modularity_in_software.pdf
Path: Wisconsin Milwaukee >> CSCI >> 780 Fall, 2009
Description: Outline The Structure and Value of Modularity in Software Design Kevin J. Sullivan, William G. Griswold 1 Introduction Background Model Approach Extension Analysis Conclusion 2 Introduction The Goals of Software Design: Increase product qua...
07-structure-and-value.pdf
Path: Wisconsin Milwaukee >> CSCI >> 780 Fall, 2009
Description: CSci 780 Advanced Software Engineering The Structure and Value of Modularity in Software Design Outline Problem Proposed Solution Background Analysis Conclusion Questions Sullivan, Griswold, Ben Hallen Baldwin and Clark Kevin J. Sullivan William G...
13-static-detection.pdf
Path: Wisconsin Milwaukee >> CSCI >> 780 Fall, 2009
Description: Outline Static Detection of Dynamic Memory Errors Problem Example (simple) Solution LCLint Solution to Example Analysis Case Studies Conclusion Discussion Static Detection of Dynamic Memory Errors 2 David Evans 10/17/2003 The Problem Man...
AlgCor2.pdf
Path: Wisconsin Milwaukee >> CSCI >> 243 Fall, 2009
Description: 1 Algorithm Correctness Problems Problem 1. AL1(M) P = 1 I = 1 while (I < 2*M) P = P * I I = I + 2 return P 1. 2. 3. 4. 5. 6. Let pn and in be the values of P and I at line 3 after the nth iteration. Question 1. Write the recursive relation for in...
final_review.pdf
Path: Wisconsin Milwaukee >> CS >> 131 Fall, 2009
Description: CS 131 Fall 2004 Final Exam Review Name: 1. Construct the corresponding truth table for the logical expression: R (P Q) (P R) 2. Construct a logic circuit for the expression: Q (P R Q) 1 3. Derive the corresponding logical expression for t...
issre_2003_student.pdf
Path: Wisconsin Milwaukee >> ISSRE >> 2003 Fall, 2009
Description: Formal Semantics for Reliability Modeling Languages: A Case Study on BDMPs Robert R. Painter Dept. of Computer Science The College of William and Mary Williamsburg, VA 23185 Abstract The informal development of high-level modeling languages results i...
demorgan.pdf
Path: Wisconsin Milwaukee >> CS >> 131 Fall, 2009
Description: 2x\'4D$)Q7Q$)XWP`QP2CHQ$24wr2G@tfV )IQ$24Q0Q$IUG5 s V V 41 V $ G E 0r 0 (1G 4 0 $ (1 0 sj 0 B @\'fwwt470)u1X@21IGVtsQ7W$2VD02BwIq7Q0@bXWP}h)XuP\'s\'424\'0)WF)\'f)XtPrt)u1F@%\'fppl2(0o\'r`bI\'Gp@\'f$ 0 (1G $ 7G j s & 4 C x 0 4 ...
schedule.pdf
Path: Wisconsin Milwaukee >> CS >> 131 Fall, 2009
Description: Computer Science 131 Spring 2006 Concepts in Computer Science Class Schedule 01/18 01/23 01/25 01/30 02/01 02/06 02/08 02/13 02/15 02/20 02/22 02/27 03/01 03/06 03/08 03/13 03/15 03/20 03/22 03/27 03/29 04/03 04/05 04/10 04/12 04/17 04/19 04/24 04/2...
syllabus.pdf
Path: Wisconsin Milwaukee >> CS >> 131 Fall, 2009
Description: Computer Science 131 Spring 2006 Concepts in Computer Science Robert Painter rrpain@cs.wm.edu 102 McGlothlinStreet Hall http:/www.cs.wm.edu/rrpain/teaching/cs131/ Class Time: MW 2:00 - 2:50 Oce Hours: MW 10:00 - 11:00, T 10:00 - 12:00 or by appointm...
finalReview.pdf
Path: Wisconsin Milwaukee >> CS >> 131 Fall, 2009
Description: Final Review 1. Know the following terms and their denitions: computer science, algorithm, boolean algerbra, boolean operators, boolean variables, logical expressions, truth tables, circuits, tautology, contradiction, ip-op, memory, bit, byte, kiloby...
lecture-5-grayscale.pdf
Path: Wisconsin Milwaukee >> CSCI >> 243 Fall, 2009
Description: CSci 243 Discrete Structures Lecture 5 Logic 1 9/6/2002 1 Outline Introduction, Propositions Compound Propositions Truth Tables Tautologies and Contradictions Predicates and Quantification 9/6/2002 2 Introduction to Logic Statements are ei...
19-model-checking.pdf
Path: Wisconsin Milwaukee >> CSCI >> 780 Fall, 2009
Description: Outline CSci 780 Advanced Software Engineering Background: Model Checking Event-Driven Systems SCR CTL Model Checking SCR Specifications Case Studies Evaluation and Conclusion Discussion State-Based Model Checking of Event-Driven System Requ...
lab5.pdf
Path: Wisconsin Milwaukee >> M >> 111 Fall, 2009
Description: Fall 2008 Math 111 Lab 6 Linear Approximation In an earlier lab, we used the divided dierence, f (x) f (a) xa (with x a = 0) to estimate the value of the derivative, f (a) = lim f (x) f (a) , xa xa f (x) f (a) , xa (1) In other words, we are e...
von-hardenberg-2001-PRL.pdf
Path: Wisconsin Milwaukee >> MATH >> 490 Fall, 2009
Description: VOLUME 87, NUMBER 19 PHYSICAL REVIEW LETTERS 5 NOVEMBER 2001 Diversity of Vegetation Patterns and Desertication J. von Hardenberg,1,4 E. Meron,1,3 M. Shachak,2 and Y. Zarmi1,3 1 Department of Solar Energy and Environmental Physics, BIDR, Ben Guri...
lect04.pdf
Path: Wisconsin Milwaukee >> MATH >> 490 Fall, 2009
Description: A review of stability and dynamical behaviors of dierential equations: u = f (u, v), t system of ODEs: vt = g(u, v), scalar ODE: ut = f (u), reaction-diusion equation: ut = Du + f (u), x , with boundary condition reaction-diusion system: u = D u...
problem-set-6.pdf
Path: Wisconsin Milwaukee >> MATH >> 410 Fall, 2009
Description: Problem Set 6 Discussion: Nov. 6 Discussion Problems 1. (a) Prove that for a, b, c > 0 satisfying (1 + a)(1 + b)(1 + c) = 8, then abc 1. (b) Prove that for a, b, c > 0, then (a2 b + b2 c + c2 a)(a2 c + b2 a + c2 b) 9a2 b2 c2 . (Carolyn) 2. Given th...
problem-set-5.pdf
Path: Wisconsin Milwaukee >> MATH >> 410 Fall, 2009
Description: Problem Set 5 Discussion Problems 1. (VT 1983) Let f (x) = 1/x and g(x) = 1 x for x (0, 1). List all distinct functions that can be written in the form f g f g f g f where represents composition. Write each function in the form (ax + b)/...
pigeon-hole.pdf
Path: Wisconsin Milwaukee >> MATH >> 410 Fall, 2009
Description: Pigeonhole Principle The pigeonhole principle states that if n pigeons are put into m pigeonholes, and if n > m, then at least one pigeonhole must contain more than one pigeon. Another way of stating this would be that m holes can hold at most m obje...
Horvath-Toth.pdf
Path: Wisconsin Milwaukee >> MATH >> 490 Fall, 2009
Description: Turing patterns in a single-step autocatalytic reaction Dezso Horvath and Agota Toth Department of Physical Chemistry, Jozsef Attila University, P.O. Box 105, Szeged, H-6701, Hungary Stable Turing patterns are presented in the simplest reaction...
lab3.pdf
Path: Wisconsin Milwaukee >> M >> 111 Fall, 2009
Description: Fall 2008 Math 111 Lab 4 Exploring the Derivative The derivative of a function is one of the most powerful tools in mathematics. It is often invaluable for investigations in both the physical, biological, and, to a lesser extent, the social science...
lab7.pdf
Path: Wisconsin Milwaukee >> M >> 111 Fall, 2009
Description: Fall 2008 Math 111 Lab 9 Newtons Method In almost every application of mathematics, it is necessary to solve equations. When there is only one independent variable, the equation may be expressed in the form f (x) = 0, where f (x) is usually a dieren...
English-Proposed.pdf
Path: N.E. Illinois >> AY >> 12122006 Fall, 2009
Description: English Language Arts with Teacher Certification reflecting changes to the English Major English Language Arts Option for Teacher Certification The English Language Arts Certification Option is for students who would like to major in English and gain...
English-Proposed.pdf
Path: N.E. Illinois >> AY >> 20062007 Fall, 2009
Description: English Language Arts with Teacher Certification reflecting changes to the English Major English Language Arts Option for Teacher Certification The English Language Arts Certification Option is for students who would like to major in English and gain...
excactionpraxis.pdf
Path: N.E. Illinois >> MAR >> 2004 Fall, 2009
Description: MEMORANDUM TO: FROM: DATE: RE: Vice President Lord Charles A. Rohn, Dean College of Education and Professional Studies March 3, 2004 Executive Action Item I concur with Dr. Shanks recommendation to revise the catalog regarding the requirement for ...
stoll.pdf
Path: Wisconsin Milwaukee >> JAN >> 05 Fall, 2009
Description: Volume LXII, Number 1 William and Mary Quarterly Reviews of Books The Great Meadow: Farmers and the Land in Colonial Concord. By BRIAN DONAHUE. New Haven, Conn.: Yale University Press, 2004. 344 pages. $35.00 (cloth). Reviewed by Steven Stoll, Yal...
delbourgo.pdf
Path: Wisconsin Milwaukee >> OCT >> 06 Fall, 2009
Description: Volume LXIII, Number 4 William and Mary Quarterly Reviews of Books American Curiosity: Cultures of Natural History in the Colonial British Atlantic World. By SUSAN SCOTT PARRISH. Chapel Hill: University of North Carolina Press, 2006. Published for...
whitman.pdf
Path: Wisconsin Milwaukee >> JAN >> 06 Fall, 2009
Description: Volume LXIII, Number 1 William and Mary Quarterly Reviews of Books Slave Country: American Expansion and the Origins of the Deep South. By ADAM ROTHMAN. Cambridge, Mass.: Harvard University Press, 2005. 312 pages. $35.00 (cloth). Reviewed by T. St...
tomlins.pdf
Path: Wisconsin Milwaukee >> OCT >> 05 Fall, 2009
Description: Volume LXII, Number 4 William and Mary Quarterly Reviews of Books From Tavern to Courthouse: Architecture and Ritual in American Law, 16581860. By MARTHA J. MCNAMARA. Creating the North American Landscape. Baltimore: Johns Hopkins University Press...
grubb.pdf
Path: Wisconsin Milwaukee >> OCT >> 06 Fall, 2009
Description: Volume LXIII, Number 4 William and Mary Quarterly Reviews of Books Eighteenth-Century Criminal Transportation: The Formation of the Criminal Atlantic. By GWENDA MORGAN and PETER RUSHTON. New York: Palgrave Macmillan, 2004. 250 pages. $69.95 (cloth...
frey.pdf
Path: Wisconsin Milwaukee >> APR >> 04 Fall, 2009
Description: Volume LXI, Number 2 William and Mary Quarterly Reviews of Books Africans in Colonial Mexico: Absolutism, Christianity, and Afro-Creole Consciousness, 15701640. By HERMAN L. BENNETT. (Bloomington and Indianapolis: Indiana University Press, 2003. P...
egerton.pdf
Path: Wisconsin Milwaukee >> JAN >> 06 Fall, 2009
Description: Volume LXIII, Number 1 William and Mary Quarterly Reviews of Books Avengers of the New World: The Story of the Haitian Revolution. By LAURENT DUBOIS. Cambridge, Mass.: Harvard University Press, 2004. 384 pages. $29.95 (cloth), $17.95 (paper). A Co...
eltis.pdf
Path: Wisconsin Milwaukee >> JAN >> 04 Fall, 2009
Description: Volume LX1, Number 1 William and Mary Quarterly Tales from the Ships David Eltis, Emory University Reviews of Books A Slaving Voyage to Africa and Jamaica: The Log of the Sandown, 17931794. Edited by BRUCE L. MOUSER. (Bloomington: Indiana Universi...
main.pdf
Path: Wisconsin Milwaukee >> APR >> 06 Fall, 2009
Description: Volume LXIII, Number 2 William and Mary Quarterly They Were All Atlanticists Then Reviews of Books Gloria L. Main, University of Colorado, Boulder The Early Modern Atlantic Economy. Edited by JOHN J. MCCUSKER and KENNETH MORGAN. Cambridge: Cambrid...
griffin.pdf
Path: Wisconsin Milwaukee >> OCT >> 04 Fall, 2009
Description: Volume LXI, Number 4 William and Mary Quarterly Reviews of Books Atlantic Virginia: Intercolonial Relations in the Seventeenth Century. By APRIL LEE HATFIELD. (Philadelphia: University of Pennsylvania Press, 2004. Pp. 312. $39.95.) Reviewed by Pat...
