Profiling Initialisation Behaviour in Java

Freshly created objects are a blank slate: their mutable state and their constant properties must be initialised before they can be used. Programming languages like Java typically support object initialisation by providing constructor methods. This thesis examines the actual initialisation of objects in real-world programs to determine whether constructor methods support the initialisation that programmers actually perform. Determining which object initialisation techniques are most popular and how they can be identified will allow language designers to better understand the needs of programmers, and give insights that VM designers could use to optimise the performance of language implementations, reduce memory consumption, and improve garbage collection behaviour. Traditional profiling typically either focuses on timing, or uses sampling or heap snapshots to approximate whole program analysis. Classifying the behaviour of objects throughout their lifetime requires analysis of all program behaviour without approximation. This thesis presents two novel whole-program object profilers: one using purely class modification (#prof ), and a hybrid approach utilising class modification and JVM support (rprof ). #prof modifies programs using aspect-oriented programming tools to generate and aggregate data and examines objects that enter different collections to determine whether correlation exists between initialisation behaviour and the use of equality operators and collections. rprof confirms the results of an existing static analysis study of field initialisation using runtime analysis, and provides a novel study of object initialisation behaviour patterns

Going Stupid with EcoLab

In 2005, Railsback et al. proposed a very simple model ({\em Stupid Model}) that could be implemented within a couple of hours, and later extended to demonstrate the use of common ABM platform functionality. They provided implementations of the model in several agent based modelling platforms, and compared the platforms for ease of implementation of this simple model, and performance. In this paper, I implement Railsback et al's Stupid Model in the EcoLab simulation platform, a C++ based modelling platform, demonstrating that it is a feasible platform for these sorts of models, and compare the performance of the implementation with Repast, Mason and Swarm versions

Model-based tool support for Tactical Data Links: an experience report from the defence domain

The Tactical Data Link (TDL) allows the exchange of information between cooperating platforms as part of an integrated command and control (C2) system. Information exchange is facilitated by adherence to a complex, message-based protocol defined by document-centric standards. In this paper, we report on a recent body of work investigating migration from a document-centric to a model-centric approach within the context of the TDL domain, motivated by a desire to achieve a positive return on investment. The model-centric approach makes use of the Epsilon technology stack and provides a significant improvement to both the level of abstraction and rigour of the network design. It is checkable by a machine and, by virtue of an MDA-like approach to the separation of domains and model transformation between domains, is open to integration with other models to support more complex workflows, such as by providing the results of interoperability analyses in human-readable domain-specific reports conforming to an accepted standard

Towards an embedded real-time Java virtual machine

Most computers today are embedded, i.e. they are built into some products or system that is not perceived as a computer. It is highly desirable to use modern safe object-oriented software techniques for a rapid development of reliable systems. However, languages and run-time platforms for embedded systems have not kept up with the front line of language development. Reasons include complex and, in some cases, contradictory requirements on timing, concurrency, predictability, safety, and flexibility. A carefully tailored Java virtual machine (called IVM) is proposed as an approach to overcome these difficulties. In particular, real-time garbage collection has been considered an essential part. The set of bytecodes has been revised to require less memory and to facilitate predictable execution. To further reduce the memory footprint, the class loader can be located outside the embedded processor. Since the accomplished concurrency is crucial for the function of many embedded applications, the scheduling can be defined on the application level in Java. Finally considering future needs for flexibility and on-line configuration of embedded system, the IVM has a unique structure with which, for instance, methods being objects that can be replaced and GCed. The approach has been experimentally verified by a full prototype implementation of such a virtual machine. By making the prototype available for development of real products, this in turn has confronted the solutions with real industrial demands. It was found that the IVM can be easily integrated in typical systems today and the mentioned requirements are fulfilled. Based on experiences from more than 10 projects utilising the novel Java-oriented techniques, there are reasons to believe that the proposed approach is very promising for future flexible embedded systems

Software Performance Engineering using Virtual Time Program Execution

In this thesis we introduce a novel approach to software performance engineering that is based on the execution of code in virtual time. Virtual time execution models the timing-behaviour of unmodified applications by scaling observed method times or replacing them with results acquired from performance model simulation. This facilitates the investigation of "what-if" performance predictions of applications comprising an arbitrary combination of real code and performance models. The ability to analyse code and models in a single framework enables performance testing throughout the software lifecycle, without the need to to extract performance models from code. This is accomplished by forcing thread scheduling decisions to take into account the hypothetical time-scaling or model-based performance specifications of each method. The virtual time execution of I/O operations or multicore targets is also investigated. We explore these ideas using a Virtual EXecution (VEX) framework, which provides performance predictions for multi-threaded applications. The language-independent VEX core is driven by an instrumentation layer that notifies it of thread state changes and method profiling events; it is then up to VEX to control the progress of application threads in virtual time on top of the operating system scheduler. We also describe a Java Instrumentation Environment (JINE), demonstrating the challenges involved in virtual time execution at the JVM level. We evaluate the VEX/JINE tools by executing client-side Java benchmarks in virtual time and identifying the causes of deviations from observed real times. Our results show that VEX and JINE transparently provide predictions for the response time of unmodified applications with typically good accuracy (within 5-10%) and low simulation overheads (25-50% additional time). We conclude this thesis with a case study that shows how models and code can be integrated, thus illustrating our vision on how virtual time execution can support performance testing throughout the software lifecycle

On Leveraging Tests to Infer Nullable Annotations

Issues related to the dereferencing of null pointers are a pervasive and widely studied problem, and numerous static analyses have been proposed for this purpose. These are typically based on dataflow analysis, and take advantage of annotations indicating whether a type is nullable or not. The presence of such annotations can significantly improve the accuracy of null checkers. However, most code found in the wild is not annotated, and tools must fall back on default assumptions, leading to both false positives and false negatives. Manually annotating code is a laborious task and requires deep knowledge of how a program interacts with clients and components. We propose to infer nullable annotations from an analysis of existing test cases. For this purpose, we execute instrumented tests and capture nullable API interactions. Those recorded interactions are then refined (santitised and propagated) in order to improve their precision and recall. We evaluate our approach on seven projects from the spring ecosystems and two google projects which have been extensively manually annotated with thousands of @Nullable annotations. We find that our approach has a high precision, and can find around half of the existing @Nullable annotations. This suggests that the method proposed is useful to mechanise a significant part of the very labour-intensive annotation task

Towards Superinstructions for Java Interpreters

The Java Virtual Machine (JVM) is usually implemented by an interpreter or just-in-time (JIT) compiler. JITs provide the best performance, but interpreters have a number of advantages that make them attractive, especially for embedded systems. These advantages include simplicity, portability and lower memory requirements. Instruction dispatch is responsible for most of the running time of efficient interpreters, especially on pipelined processors. Superinstructions are an important optimisation to reduce the number of instruction dispatches. A superinstruction is a new Java instruction which performs the work of a common sequence of instructions. In this paper we describe work in progress on the design and implementation of a system of superinstructions for an efficient Java interpreter for connected devices and embedded systems. We describe our basic interpreter, the interpreter generator we use to automatically create optimised source code for superinstructions, and discuss Java specific issues relating to superinstructions. Our initial experimental results show that superinstructions can give large speedups on the SPECjvm98 benchmark suite

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Implementation of a RANLUX Based Pseudo-Random Number Generator in FPGA Using VHDL and Impulse C

Monte Carlo simulations are widely used e.g. in the field of physics and molecular modelling. The main role played in these is by the high performance random number generators, such as RANLUX or MERSSENE TWISTER. In this paper the authors introduce the world's first implementation of the RANLUX algorithm on an FPGA platform for high performance computing purposes. A significant speed-up of one generator instance over 60 times, compared with a graphic card based solution, can be noticed. Comparisons with concurrent solutions were made and are also presented. The proposed solution has an extremely low power demand, consuming less than 2.5 Watts per RANLUX core, which makes it perfect for use in environment friendly and energy-efficient supercomputing solutions and embedded systems