Dissertation Submitted In Partial Fulfillment Of The Requirements

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This suggests that farmers using IPM technology benefit from income gains, and higher incomes improve the economic availability to food but not food access.We use cookies to make interactions with our website easy and meaningful, to better understand the use of our services, and to tailor advertising.For further information, including about cookie settings, please read our Cookie Policy .Design: Cross-sectional descriptive;observational study Setting: Kenvatta National Hospital Methods: This descriptive survey of intensive care givers at KNH used a size 7.5 ETT with a large volume low pressure cuff (portex) in a 2 cm diameter rigid tube as a tracheal…Partial fulfillment implies that there are other requirements that need to be satisfied in order to receive the degree.PERFORMANCE MEASUREMENT OF INTERPRETED, JUST-IN-TIME COMPILED, AND DYNAMICALLY COMPILED EXECUTIONS BY TIA NEWHALL A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Sciences) at the University of Wisconsin Madison 1999 i Acknowledgments During the course of my graduate career I have benefited from the help, support, advice and suggestions of a great many people.My thesis advisor, Bart Miller, provided years of technical and professional guidance, advice, and support.To evaluate the impact of IPM on food security the difference-in-difference method (DD) was used.The results indicate that 67 percent of IPM participants in Mwala and 75 percent of nonparticipants in Kangundo were food secure as they had attained the 2,250 Kcal threshold recommended by the Kenya National Bureau of Statistics (KNBS).Most of all, I would not have been able to accomplish my goals without the love, support and encouragement of Martha Townsend and the rest of my family. ii Contents Acknowledgments i Contents ii List of Figures vi 1 Introduction Motivation Performance Measurement of Interpreted Executions Performance Measurement of Application s with Multiple Execution Forms Summary of Results Organization of Dissertation Related Work Performance tools for interpreted and JIT compiled executions Traditional performance tools Tools that Map Performance Data to User s View of Program Tools that can See Inside the Kernel Tools that Expose Abstractions from User-Level Libraries Conclusions Describing Performance Data that Represent VM-AP Interactions Representing an Interpreted Execution Representing a Program Execution Representing the VM and AP Programs Representing Interacting Programs Representing Constrained Parts of Program Executions Active Resources and Constraint Functions Constraint Operators Properties of Constraint Operators Foci iii 3.3 Representing Performance Data from Interpreted Executions Using Foci to Constrain Performance Data Using Metrics to Constrain Performance Data Metric Functions for Interpreted Executions Combining Metrics with Foci from VM runs AP Performance Data Associated with Asynchronous Events Conclusions Paradyn-J: A Performance Tool for Measuring Interpreted Java Executions Paradyn-J s Implementation The Java Virtual Machine Parsing Java.class Files and Method Byte-codes Dynamic Instrumentation for VM Code Transformational Instrumentation for AP Code Java Interpreter-Specific Metrics Modifying the Performance Consultant to Search for Java Bottlenecks Transformational Instrumentation Costs Advantages and Disadvantages of Transformationa Instrumentation Performance Tuning Study of an Interpreted Java Application Conclusions Motivational Example Performance Measurement Study Discussion Describing Performance Data from Applications with Multiple Execution Forms Representing the Application s Multiple Execution Forms Representing Different Forms of an AP Code Object Resource Mapping Functions Representing Performance Data Representing Form-Dependent Performance Data Representing Form-Independent Performance Data Representing Transformational Costs Changes to Paradyn-J to Support Measuring Dynamically iv Compiled Java Executions Simulating Dynamic Compilation Modifications to Paradyn-J Performance Tuning Study of a Dynamically Compiled Java Application Our Performance Data and VM Developers Conclusions Lessons Learned from Paradyn-J s Implementation Issues Related to the Current Implementation of Paradyn-J Alternative Ways to Implement a Tool Based on Our Model Requirements for Implementing Our Model Implement as a Special Version of VM Using the JVMPI Interface Changes to JVMPI for a more Complete Implementation Conclusions Conclusion Thesis Summary Future Directions References vi List of Figures 1.1 Compiled application s execution vs.Interpreted application s execution Dynamic Compilation of AP byte-codes Example Resource Classes Example of Types of resource class instances in different resource hierarchies Example of resource hierarchies for the virtual machine and the application program Resource hierarchies representing the interpreted execution Active Definitions for instances of different Resource classes Generic algorithm for implementing a Resource Class constrain method Constraint tests for constraints combined with constraint operators An example of applying constraint operators for programs with multiple threads Properties of Constraint Operators Example Metric Definitions Memory Areas of the Java Virtual Machine Dynamic Instrumentation for Java VM code Transformational Instrumentation for Java application byte-codes Java Interpreter Specific Metrics Performance Consultant search showing VM-specific bottlenecks in a neural network Java application Timing measures for a Transformational Instrumentation request Timing measures of Transformational Instrumentation perturbation Performance Data showing part of transformational instrumentation perturbation Resource hierarchies from interpreted Java execution 4.10 High-level performance characteristics of the interpreted Java program Performance data showing VM overhead associated with the Java application s execution The fraction of CPU time spent in different AP methods VM method call overhead associated with the Sim.class Performance Data showing which methods are called most frequently Performance results from different versions of the application Table showing the number of objects created/second in AP classes and methods Performance data showing which objects are created most frequently Execution time (in seconds) of each Java kernel run by Exact VM comparing interpreted Java (Intrp column) to dynamically compiled Java (Dyn column) Types of resource instances that the APCode hierarchy can contain The APCode hierarchy after method foo is compiled at run-time Example of a 1-to-1 resource mapping : to-N mappings resulting from method in-lining and specialization: N-to-1 mappings resulting from method in-lining with course granularity or mingled code: Using resource mapping functions to map performance data Performance data associated with a transformed AP code object Performance Data measuring transformation times of seven methods from a Java neural network application program Simulation of dynamic compiling method foo Performance data for the updateweights method from the dynamically compiled neural network Java application Performance data for the updateweights method from the dynamically compiled neural network Java application Performance data for method calculatehiddenlayer Performance data for method calculatehiddenlayer after removing some object creates Total execution times under Exact VM for the original and the tuned versions of the vii 1 Chapter 1 Introduction With the increasing popularity of Java, interpreted, just-in-time compiled and dynamically compiled executions are becoming a common way for application programs to be executed.


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