EPL - Equational Programming Language Parsing and Dimension Propagation

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.

Compiler design plays an important role in ensuring that the translation of the programs written in high-level language into executable code is correct. However, in todays’ safety-critical environments, security gaps, visible and hidden defects in compiler models are liability factors that needed to be addressed in the process of examining that a system meets specifications and requirements of its intended objectives. Nevertheless, defects in this model are errors in coding which bring about incorrect or unexpected results from a compiler design that does not meet the actual requirements. Hence, this paper developed a novel approach by integrating the computational power of Communicating Stream X-Machine (CSXM) to address the problem associated with compiler correctness. In addition, this paper outlines the development of a novel compiler design called X-compiler, emphasizing its important role in software development. CSXM technique was implemented in Visual Studio (2022) to develop the X-compiler. The X-compiler utilize state-of-the-art techniques to translate program source code written in high-level programming language which is the blend of C# and Python programming language into executable code. The development process includes the lexical analysis, syntax parsing, semantic analysis, and code generation ensuring the correctness of the X-compiler. The results of the compiled code were run to produce output in command Windows environment for user interactions. Also, highlights the potential for enhanced language interoperability and the development of efficient compiler that leverage the strengths of multiple programming paradigms. Overall, the study on CSXM contributed to a deeper understanding of concurrent systems, while the design and implementation of the compiler showcased the feasibility of creating a synergistic blend of C# and Python programming languages.

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.

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.

The level of support for high level programming languages (HLPLs), despite the claim of support by some of the newly introduced microprocessors, is inadequate despite the falling (rising) cost of hardware (software). Computer systems that are to be used to run programs written exclusively in HLPLs have a need for a multiprocessor/multiarchitecture microprocessor design (M3D); the personal/desk-top computers are prime examples. The HLPLs that will be used in programming these systems will be grouped according to their structures. Each group of languages will require their own instruction set; the hardware and software needs will determine the type of architecture configuration, i.e. aspects such as: register-to-register, memory-to-memory, stack, or a combination of these. The overall system should be divided up into functional units, e.g. ALU, compiler/translator, etc. Each functional or subfunctional unit will be implemented in software, firmware, hardware, or a combination of these with implementation decision based on the speed criticality, cost, technology, and the need to modify the unit. This approach will lead to a cop-down-design, and will also reduce the semantic gap, software and system cost, and down-time.

We execute the quantum eraser, the Elitzur–Vaidman bomb, and the Hardy’s paradox experiment using high-level programming language on a generic, gate-based superconducting quantum processor made publicly available by IBM. The quantum circuits for these experiments use a mixture of one-qubit and multi-qubit gates and require high entanglement gate accuracy. The results aligned with theoretical predictions of quantum mechanics to high confidence on circuits using up to 3 qubits. The power of quantum computers and high-level language as a platform for experimenting and studying quantum phenomena is henceforth demonstrated.

In this paper we advocate that it is time for a radical rethinking of database systems design. Developers should be able to leverage high-level programming languages without having to pay a price in efficiency. To realize our vision of abstraction without regret , we present LegoBase, a query engine written in the high-level programming language Scala. The key technique to regain efficiency is to apply generative programming: the Scala code that constitutes the query engine, despite its high-level appearance, is actually a program generator that emits specialized, low-level C code. We show how the combination of high-level and generative programming allows to easily implement a wide spectrum of optimizations that are difficult to achieve with existing low-level query compilers, and how it can continuously optimize the query engine. We evaluate our approach with the TPC-H benchmark and show that: (a) with all optimizations enabled, our architecture significantly outperforms a commercial in-memory database system as well as an existing query compiler, (b) these performance improvements require programming just a few hundred lines of high-level code instead of complicated low-level code that is required by existing query compilers and, finally, that (c) the compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for efficiently compiling query engines.

If you really want to get to the “heart” of a computer system then it is necessary to learn to talk to it in, as near as possible, a language that it understands. Using high-level programming languages such as MBASIC means that you are talking to the computer through an interpreter and hence all the instructions that you issue in a program take that much longer to be carried out. That is why we use a compiler; see chapter 8. But even if we use a compiled language we cannot control a number of aspects of the computer’s operation at what we could call the “bit level”. This is the lowest level of communication with the microprocessor and in the past, when computers were still in their infancy, this was the only way of programming. It was actually performed in the binary code of zeros and ones. This is not surprising since this is the only kind of code that the processor understands. High-level languages have taken a lot of the hard work out of programming, but a lot of detail has been lost on the way.

Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend. In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level programming language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes database systems code by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other. We evaluate our approach with the TPC-H benchmark and show that (a) with all optimizations enabled, our architecture significantly outperforms a commercial in-memory database as well as an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches. (c) These optimizations may potentially come at the cost of using more system memory for improved performance. (d) The compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for compiling query engines.

Security types preserving compilation

: Agent oriented programming was proposed as a high-level programming language, in which a programmer is given the opportunity to communicate with other programs in a uniform, high-level language. Furthermore, the programmer could explicitly represent in the program (or 'agent') the relationship with other program (or 'agent'), including the beliefs about the other agents and the obligations made to them. Our hypothesis was that such 'mental-level' design would provide a powerful abstraction that would enable the analysis and even design of complex distributed systems. In addition to such coordination via high-level modeling and communication, we were interested in global mechanisms that eliminate the need for explicit coordination in the place.

The ULowell Data Flow Project combines dataflow hardware, dataflow software, and a data flow development environment. The hardware component, a data flow coprocessor card, utilizes seven NEC 7281 Image Pipelined Processors. Each chip models a static dataflow computation and is programmable. The coprocessor was designed and implemented by ULowell personnel and uses an AMIGA 2000 as a host. The software component currently consists of a library of data flow image processing algorithms and an evolving software development environment. Currently the image processing library includes typical imaging operations such as: edge enhancement, filtering, histogramming, and affine transformation. The software development environment (SDE) is depicted in Figure 1. The SDE contains a Data Flow Graph Editor which programmers use to design their data flow code. The data flow algorithm can then be converted directly into NEC data flow assembly code which is then assembled using the ULowell Data Flow Assembler. The assembler differs from NEC's assembler in that it can produce relocatable code. The object code output of the assembler is given to the Data Flow Resource Manager. The resource manager monitors the available resources within the chips. Thus, if a single process does not totally utilize a chip's table space or memory, then a second process may be concurrently loaded onto the same chip. The ability to have multiple data flow processes coexisting within a single chip is unique to the ULowell environment. The data flow resource manager can download and monitor code to either the hardware or to a software simulator/debugger. The simulator/debugger can monitor and trace data flow tokens as they proceed through the chip. Output of the simulator/debugger can be fed back into the data flow graph editior to visually trace token flow through the original program design. The last component of our SDE is a High Level Language translator which can compile programs written in SISAL, a high level language for parallel computation, down to NEC 7281 assembly language. We have already implemented the relocatable assembler, and the data flow resource manager. Prototype versions of the high level translator, simulator/debugger and graph/editor are currently being implemented.

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.