Assuring Correctness, Testing, and Verification of X-Compiler by Integrating Communicating Stream X-Machine

We study the problem of generating interpretable and verifiable policies for Reinforcement Learning (RL). Unlike the popular Deep Reinforcement Learning (DRL) paradigm, in which the policy is represented by a neural network, the aim of this work is to find policies that can be represented in highlevel programming languages. Such programmatic policies have several benefits, including being more easily interpreted than neural networks, and being amenable to verification by scalable symbolic methods. The generation methods for programmatic policies also provide a mechanism for systematically using domain knowledge for guiding the policy search. The interpretability and verifiability of these policies provides the opportunity to deploy RL based solutions in safety critical environments. This thesis draws on, and extends, work from both the machine learning and formal methods communities.

The relevance. Lack of experimental data that allow developing a scientifically based method for calculating and designing film-type reactors, which are also used to produce alkylbenzenesulfonic acids. These acids, in their turn, are currently the main components of synthetic detergents. The issue of increasing reactor equipment efficiency can be most effectively solved using mathematical models built on a physical and chemical basis. The aim. Development of a mathematical model of alkylbenzenes sulfonation, taking into account a substance mass transfer from a gas phase to a liquid phase. Software implementation of the developed model, as well as the use of the developed mathematical model for studying the influence of the process parameters on its efficiency. Object. Alkylbenzenes sulfonation with sulfuric anhydride in a multitube film reactor. Methods. Mathematical modeling is used to perform all computational operations, a modern high-level general-purpose programming language with automatic memory management is used. The quantum-chemical methods for determining thermodynamic parameters of chemical reactions were used. Results. The paper considers the principles of constructing a mathematical model of sulfonation. The authors have developed the calculation program in the Python programming language and assessed the accuracy of description of a real process and the influence of the system technological parameters on a product yield and quality, taking into account a substance interfacial transfer. The system of practical recommendations for improving the alkylbenzenes sulfonation resource efficiency was developed. The mathematical model adequately describes the process. The calculated data are compared with the real data from the operating unit for alkylbenzenes sulfonation with sulfuric anhydride.

The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.

GPUs and other accelerators are popular devices for accelerating compute-intensive, parallelizable applications. However, programming these devices is a difficult task. Writing efficient device code is challenging, and is typically done in a low-level programming language. High-level languages are rarely supported, or do not integrate with the rest of the high-level language ecosystem. To overcome this, we propose compiler infrastructure to efficiently add support for new hardware or environments to an existing programming language. We evaluate our approach by adding support for NVIDIA GPUs to the Julia programming language. By integrating with the existing compiler, we significantly lower the cost to implement and maintain the new compiler, and facilitate reuse of existing application code. Moreover, use of the high-level Julia programming language enables new and dynamic approaches for GPU programming. This greatly improves programmer productivity, while maintaining application performance similar to that of the official NVIDIA CUDA toolkit.

In the past two years the processing power of video graphics cards has quadrupled and is approaching super computer levels. State-of-the-art graphical processing units (GPU) boast of theoretical computational performance in the range of 1.5 trillion floating point operations per second (1.5 Teraflops). This processing power is readily accessible to the scientific community at a relatively small cost. High level programming languages are now available that give access to the internal architecture of the graphics card allowing greater algorithm optimization. This research takes memory access expensive portions of an image-based iris identification algorithm and hosts it on a GPU using the C++ compatible CUDA language. The selected segmentation algorithm uses basic image processing techniques such as image inversion, value squaring, thresholding, dilation, erosion and memory/computationally intensive calculations such as the circular Hough transform. Portions of the iris segmentation algorithm were accelerated by a factor of 77 over the 2008 GPU results. Some parts of the algorithm ran at speeds that were over 1600 times faster than their CPU counterparts. Strengths and limitations of the GPU Single Instruction Multiple Data architecture are discussed. Memory access times, instruction execution times, programming details and code samples are presented as part of the research.

Hydrologists need simple, yet powerful, open source framework for developing and testing mathematical models. Such framework should ensure long-term interoperability and high scalability. This can be done by implementation of the existing, already tested standards. At the moment two interesting options exist: Open Modelling Interface (OpenMI) and Object Modeling System (OMS). OpenMI was developed within the Fifth European Framework Programme for integrated watershed management, described in the Water Framework Directive. OpenMI interfaces are available for the C# and Java programming languages. OpenMI Association is now in the process of agreement with Open Geospatial Consortium (OGC), so the spatial standards existing in OpenMI 2.0 should be better implemented in the future. The OMS project is pure Java, object-oriented modeling framework coordinated by the U.S. Department of Agriculture. Big advantage of OMS compared to OpenMI is its simplicity of implementation. On the other hand, OpenMI seems to be more powerful and better suited for hydrological models. Finally, OpenMI model was selected as the base interface for the proposed open source hydrological framework. The existing hydrological libraries and models focus usually on just one GIS package (HydroFOSS – GRASS) or one operating system (HydroDesktop – Microsoft Windows). The new hydrological framework should break those limitations. To make hydrological models’ implementation as easy as possible, the framework should be based on a simple, high-level computer language. Low and mid-level languages, like Java (SEXTANTE) or C (GRASS, SAGA) were excluded, as too complicated for regular hydrologist. From popular, high-level languages, Python seems to be a good choice. Leading GIS desktop applications – GRASS and QGIS – use Python as second native language, providing well documented API. This way, a Python-based hydrological library could be easily integrated with any GIS package supporting this programming language. As the OpenMI 2.0 standard supported interfaces only for Java and C#, the Python interface for OpenMI standard, presented in this paper, is the first step done towards the open and interoperable hydrological framework. GIS-related issues of the OpenMI 2.0 standard are also outlined and discussed.

We propose a simplified high-level programming language based on blocks and links dragged on a workspace which generates the skeleton code for robotic applications involving different types of robots. In order to develop such a high-level programming language that still can guarantee flexibility in term of implementation, our approach takes advantage of the robot operating system (ROS). ROS is a open source meta-operating system that provides a message passing structure between different processes (or nodes) across a network (inter-process communication). In our framework, we consider a hierarchical approach in which at the base there is ROS that allows inter-process communication between nodes in a robot and on the top we create a high-level language that interacts with ROS and thus with the real robot. The high-level language can be viewed as an extra layer added to simplify lower level code generation.

: This report gives a detailed description of parsing and dimensionality propagation for the Equational Programming Language compiler. The Equational Programming Language, called EPL, is a very high-level declarative language designed to specify parallel and real-time computations. The EPL compiler translates equational specifications into procedural, high level language (currently C). The compiler itself is written in C for UNIX based systems. Automated tools YACC and LEX has been used for lexical analysis and parsing. Detailed description is given of the design and implementation of the algorithms for building different structures (such as symbol table, equation trees, etc.). These structures are used by subsequent post-parsing analysis and code-generation phases of the EPL compiler. The description of the algorithm for dimensionality propagation is also included.

This textbook is intended for an introductory course on Compiler Design, suitable for use in an undergraduate programme in computer science or related fields.Introduction to Compiler Design presents techniques for making realistic, though non-optimizing compilers for simple programming languages using methods that are close to those used in real compilers, albeit slightly simplified in places for presentation purposes. All phases required for translating a high-level language to machine language is covered, including lexing, parsing, intermediate-code generation, machine-code generation and register allocation. Interpretation is covered briefly.Aiming to be neutral with respect to implementation languages, algorithms are presented in pseudo-code rather than in any specific programming language, and suggestions for implementation in several different language flavors are in many cases given. The techniques are illustrated with examples and exercises.The author has taught Compiler Design at the University of Copenhagen for over a decade, and the book is based on material used in the undergraduate Compiler Design course there.Additional material for use with this book, including solutions toselected exercises, is available at http://www.diku.dk/~torbenm/ICD

The SYMBOL 2R Computing System represents an attempt to bridge the gap between machine languages and higher level programming languages. This gap has always limited the users' ability to communicate with the machine, thus creating a communication problem. Early attempts at solving this communication problem consisted of developing a series of user-oriented programming languages, i.e., assembler languages and higher level languages, supplemented with a host of translator programs that would transform the higher level languages into machine language. For several years it seemed that this approach represented the only economically feasible solution. In 1965, W. H. Burkhardt (2) suggested the development of high-level language computers Burkhardt did suggest that the availability of low cost components would make high-level language computers an economically feasible solution. In fact, the recent decline in the cost of components as compared with the steady, if not increasing cost of the software needed to develop and maintain compilers for high-level languages, has made the high-level language computer solution seem even more attractive.

An architectural concept called Grammar-Programming is introduced which allows computers to be constructed that can directly execute a variety of high-level languages. Representing an intermediate level between the basic hardware/firmware functions of ordinary computers and the software operations of language translators, it is shown how grammar-programs can be constructed which specify the syntax and semantics of various programming languages. The Grammar-Programmable Machine (GPM) then uses these specifications to process directly the users' high-level language programs. In the Ph.D. dissertation* upon which this abstract is based, a model is first developed for representing the syntactic and semantic characteristics of context-free language generators, and an automaton called a Syntax Network (SN) is constructed. Next a simple, statement-directed language is introduced to express the states of the syntax network and to define the actual grammar-programming language. A simulator is then implemented which is used to test the grammar-programs written for ALGOL and SNOBOL. Finally, the architectural organization for the Grammar-Programmable Machine is described at the register-transfer level. By taking advantage of its intermediate position between software compilation and hardware interpretation of high-level languages, the Grammar-Programmable Machine is able to emphasize the best features of both techniques and to achieve a potential that neither can reach individually.

An architectural concept called Grammar-Programming is introduced which allows computers to be constructed that can directly execute a variety of high-level languages. Representing an intermediate level between the basic hardware/firmware functions of ordinary computers and the software operations of language translators, it is shown how grammar-programs can be constructed which specify the syntax and semantics of various programming languages. The Grammar-Programmable Machine (GPM) then uses these specifications to process directly the users' high-level language programs. In the Ph.D. dissertation* upon which this abstract is based, a model is first developed for representing the syntactic and semantic characteristics of context-free language generators, and an automaton called a Syntax Network (SN) is constructed. Next a simple, statement-directed language is introduced to express the states of the syntax network and to define the actual grammar-programming language. A simulator is then implemented which is used to test the grammar-programs written for ALGOL and SNOBOL. Finally, the architectural organization for the Grammar-Programmable Machine is described at the register-transfer level. By taking advantage of its intermediate position between software compilation and hardware interpretation of high-level languages, the Grammar-Programmable Machine is able to emphasize the best features of both techniques and to achieve a potential that neither can reach individually.

High performance reconfigurable computers (HPRCs), which combine general-purpose processors (GPPs) and field programmable gate arrays (FPGAs), are now commercially available. These interesting architectures allow for the creation of reconfigurable processors. HPRCs have already been used to accelerate integer and fixed-point applications. However, extensive parallelism and deeply pipelined floating-point cores are necessary to make MHz-scale FPGAs competitive with GHz-scale GPPs, thus making it difficult to accelerate certain kinds of floating-point kernels. Kernels with variable length nested loops, e.g., sparse matrix-vector multiply, have been problematic because of the loop-carried dependence associated with the pipelined floating-point units. While hardware description language (HDL)-based kernels have shown moderate success in addressing this problem, the use of a high-level language (HLL)-based approach to accelerate such applications has been rather elusive. If HPRCs are to become a part of mainstream military and scientific computing, we should emphasize the use of HLL-based programming, whenever possible, rather than HDL-based hardware design. The primary reason is the increased programmer productivity associated with HLLs when compared with HDLs. For example, the floating-point addition statement z = x+y, a single line in an HLL, corresponds to hundreds of lines of HDL. In this paper, we describe the design and implementation of a sparse matrix Jacobi processor to solve systems of linear equations, Ax=b. The parallelized, deeply pipelined, IEEE-754-compliant 32-bit floating-point sparse matrix Jacobi iterative solver runs on a contemporary HPRC. The FPGA-based components are implemented using only an HLL (the C programming language) and the Carte HLL-to-HDL compiler. An HLL-based streaming accumulator allows for the implementation of fully pipelined loops and results in a 2.5-fold wall clock runtime speedup when compared with an equivalent software-only implementation.

Correctness checking of HDL-model behavior is an integral part of runtime verification of hardware. As a rule, it is based on comparing of HDL-model behavior and reference model behavior, developed in high-level programming languages. Being verified, both models are stimulated with the same input sequence; their output traces are caught and matched. Due to the abstractness of the reference model, the matching is not a trivial task as event sequences can be different and some events of one trace may miss in the other one. A methodology of runtime trace matching for hardware models of different abstraction levels is suggested in this paper. The methodology has been successfully applied to a number of industrial projects of unit-level microprocessor verification.

This thesis presents research to build and evaluate embedding of a textual form of state machines into high-level programming languages. The work entailed adding state machine syntax and code generation to the Umple model-oriented programming technology. The added concepts include states, transitions, actions, and composite states as found in the Unified Modeling Language (UML). This approach allows software developers to take advantage of the modeling abstractions in their textual environments, without sacrificing the value added of visual modeling. Our efforts in developing state machines in Umple followed a test-driven approach to ensure high quality and usability of the technology. We have also developed a syntax-directed editor for Umple, similar to those available to other high-level programming languages. We conducted a grounded theory study of Umple users and used the findings iteratively to guide our experimental development. Finally, we conducted a controlled experiment to evaluate the effectiveness of our approach. By enhancing the code to be almost as expressive as the model, we further support model-code duality; the notion that both model and code are two faces for the same coin. Systems can be and should be equally-well specified textually and diagrammatically. Such duality will benefit both modelers and coders alike. Our work suggests that code enhanced with state machine modeling abstractions is semantically equivalent to visual state machine models. The flow of the thesis is as follows; the research hypothesis and questions are presented in “Chapter 1: Introduction”. The background is explored in “Chapter 2: Background”. “Chapter 3: Syntax and semantics of simple state machines” and “Chapter 4: Syntax and semantics of composite state machines” investigate simple and composite state machines in Umple, respectively. “Chapter 5: Implementation of composite state machines” presents the approach we adopt for the implementation of composite state machines that avoids explosion of the amount of generated code. From this point on, the thesis presents empirical work. A grounded theory study is presented in “Chapter 6: A Grounded theory study of Umple”, followed by a controlled experiment in “Chapter 7: Experimentation”. These two chapters constitute our validation and evaluation of Umple research. Related and future work is presented in “Chapter 8: Related work”.