Analyzing logic programs with dynamic scheduling

This paper analyzes the impact of dynamic task scheduling, processing element allocation and data transfer management on system performance of heterogeneous MPSoCs. Therefore, all parts of a runtime scheduling unit are analyzed. Bottlenecks are identified and their complexity evaluated. Furthermore, traced information is processed with two newly introduced tools. The first one generates an annotated SDF3 file. The second one creates a static schedule which applies the same scheduling and allocation decision as the dynamic scheduler. The execution of the static schedule reduces the burden of task management to a minimum. The resulting static execution is compared with the execution of the dynamic schedule. Hence, runtime overhead of dynamic scheduling is unveiled.

This article describes the implementation of a dynamic scheduler for loading distribution between a hardware accelerator RTL and a CPU software task. The basic composition of a Xilinx-Zynq SoC device is a processing system (PS), coupled with FPGA programmable logic (PL). The two sections are connected via a number of Advanced eXtensible Interfaces. Hardware accelerators are mechanisms whereby different software algorithms are implemented register transfer logic (RTL) in the PL module. These accelerators determine an increased processing speed. In this article, we present a dynamic scheduler used for distribution of the load between the host processor and the RTL accelerator. There are situations in which even with increased processing speed of the accelerator, it cannot cope with the flow of data coming from memory system (shared memory). Therefore, it is necessary for this accelerator to be “aided” by a software module running in a CPU in the PS section. The article describes a scheduler that checks whether a hardware module for data processing meets the requirement of Hard Real Time (data are processed within a well-defined time frame), and in case it does not, it activates a software thread running on a CPU to support the hardware thread (out of the whole amount of data to be processed by the RTL thread, some of it is processed by the SW thread. Thus, the RTL thread will have less data to process). The scheduler activates the SW thread only when the system has to respond in real time and the amount of data cannot be processed within a certain time. Thus, the scheduler detects the need to activate the software thread that “helps” the hardware thread to process the data. The scheduler self-adjusts so that it executes a number of instructions in the software thread at all times, without introducing delays in running the RTL thread which is much faster. For this project PYNQ Z2 board, Vivado 2018.3 and Jupyter Notebook tools have been used.

A meta-program, regardless of the nature of the programming language, is a program whose data denotes another (object) program. The importance of meta-programming can be gauged from its large number of applications. These include compilers, interpreters, program analysers, and program transformers. Furthermore, if the object program is a logic or functional program formalizing some knowledge, then the meta-program may be regarded as a meta-reasoner for reasoning about this knowledge. In this chapter, the meta-program is assumed to be a logic program. The object program does not have to be a logic program although much of the work in this chapter assumes this. We have identified three major topics for consideration. These are the theoretical foundations of meta-programming, the suitability of the alternative meta-programming techniques for different applications, and methods for improving the efficiency of meta-programs. As with logic programs generally, meta-programs have declarative and procedural semantics. The theoretical study of meta-programming shows that both aspects of the semantics depend crucially on the manner in which object programs are represented as data in a meta-program. The second theme of the paper is the problem of designing and choosing appropriate ways of specifying important meta-programming problems, including dynamic metaprogramming and problems involving self-application. The third theme concerns efficient implementation of meta-programs. Meta-programming systems require representations with facilities that minimize the overhead of interpreting the object program. In addition, efficiency can be gained by transforming the meta-program, specializing it for the particular object program it is reasoning about. This chapter, which concentrates on these aspects of meta-programming, is not intended to be a survey of the field. A more complete survey of meta-programming for logic programming can be found in [Barklund, 1995]. Many issues in meta-programming have their roots in problems in logic which have been studied for several decades. This chapter emphasizes meta-programming solutions. It is not intended to give a full treatment of the underlying logical problems, though we try to indicate some connections to wider topics in meta-logic. The meta-programs in this chapter are logic programs based on first order logic.

Relating defeasible and normal logic programming through transformation properties

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Job-shop scheduling has challenged many researchers in that, although it belongs to a class of problems amenable to combinatorial analysis, a fully mathematical solution would not be feasible for many applications of realistic size and scope. A plethora of approaches to computer-assisted job-shop scheduling have been proposed, ranging from OR-type optimization techniques to Al/knowledge-based satisficing solutions. However, few of these approaches have had any success in delivering generic operational solutions Logica, as a part of a three-year ESPRIT project (No. 418) in open CAM systems (OCS), has developed a software tool for dynamic finite capacity job-shop scheduling (ESPRIT 1989). The approach adopted in this development combines conventional schedule generation with knowledge-based evaluation and repair, to provide an integrated decision-support tool for the managers of job-shop environments. The system can interpret disturbance information acquired from on-line shop monitoring computers, and can suggest repairs to the current schedule in order to minimize operational disruptions. In addition, the system provides automatic schedule evaluation and improvement facilities, using expert knowledge, and flexible graphical editing for manual schedule modification The work included a systematic analysis of the generic types and structures of knowledge involved in schedule evaluation and repair. It produced a set of guidelines for structured knowledge elicitation in similar application domains. Development involved integration of diverse software techniques (logic programming, relational databases, object-oriented programming, algorithmic programming) and tools (Prolog, Oracle, NeWS, ‘C). This resulted in a powerful prototyping environment for knowledge-based or conventional production scheduling applications. The range of scenarios used during the design and development of the software included one based on an automated cell with on-line computerized production monitoring systems. This was linked to output from an MRPII system, provided by a large international manufacturer of printing machinery based in the UK, who also participated in the evaluation of the results produced The results show that this hybrid approach to production scheduling is a promising basis for developing practical decision-support tools for managers of job-shops. This is because the approach can be easily configured to use various optimization algorithms or heuristics, providing performance

Large language models (LLMS) have become an essential tool to support developers using traditional text-based programming languages, but the graphical notation of the block-based Scratch programming environment inhibits the use of LLMs. To overcome this limitation, we propose the LitterBox<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> framework that extends the Scratch static code analysis tool LitterBox with the generative abilities of LLMs. By converting block-based code to a textual representation suitable for LLMs, LitterBox<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup>allows users to query LLMs about their programs, about quality issues reported by LitterBox, and it allows generating code fixes. Besides offering a programmatic API for these functionalities, LitterBox<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> also extends the Scratch user interface to make these functionalities available directly in the environment familiar to learners. The framework is designed to be easily extensible with other prompts, LLM providers, and new features combining the program analysis capabilities of LitterBox with the generative features of LLMs. We provide a screencast demonstrating the tool at https://youtu.be/RZ6E0xgrIgQ.

Instruction-level parallelism in a single stream of code for non-numerical applications has been the subject of many recent researches. This work extends the analysis to symbolic applications described with logic programming. In particular, the authors analyze the effects on performance of speculative execution, memory alias disambiguation, renaming and flow prediction. The obtained results indicate that one can reach a sustained parallelism of 4 (comparable with imperative languages), with the proper optimizations. The authors also show a comparison between static and dynamic scheduled approaches, outlining the conditions under which a dynamic solution can reach substantial improvements over a static one. In this way, they point out some important optimizations and parameters of a dynamic scheduling approach, indicating a guideline for future architectural implementations. >

Genetic programming hyper-heuristic-based solution for dynamic energy-efficient scheduling of hybrid flow shop scheduling with machine breakdowns and random job arrivals

Self-scheduling is a method for task schedul ing in parallel programs, in which each processor acquires a new block of tasks for execution whenever it becomes idle. To get the best performance, the block size must be chosen to balance the scheduling overhead against the load im balance. To determine the best block size, better analytical models of self-scheduling are needed. We present an experimental study of self-scheduling on a BBN TC2000, a NUMA machine. Previously published models to predict running time and optimal block size were tested using our experimental results. Although the models gave good predictions for small block sizes, for large block sizes the models fail, underestimating the running time by almost a factor of two. We present an upper bound on the running time and use it to explain this failure.

In this paper, we consider a wireless cloud computing system in which a profit-maximizing wireless service provider provides cloud computing services to its subscribers. In particular, we focus on batch services, which, due to their non-urgent nature, allow more scheduling flexibility than their interactive counterparts. Unlike the existing research that studied separately demand-side management and energy cost saving techniques (both of which are critical to profit maximization), we propose a provably-efficient Dynamic Scheduling and Pricing (Dyn-SP) algorithm which proactively adapts the service demand to workload scheduling in the data center and opportunistically utilizes low electricity prices to process batch jobs for energy cost saving. Without the necessity of predicting future information as assumed by some prior works, Dyn-SP can be applied to an arbitrarily random environment in which the electricity price, available renewable energy supply, and wireless network capacities may evolve over time as arbitrary stochastic processes. It is proved that, compared to the optimal offline algorithm with future information, Dyn-SP can produce a close-to-optimal longterm profit while bounding the job queue length in the data center. We also show both analytically and numerically that a desired tradeoff between the profit and queueing delay can be obtained by appropriately tuning the control parameter. Finally, we perform a simulation study to demonstrate the effectiveness of Dyn-SP.

The aim of this paper is to analyse the concept of extending the parallel logic programming paradigm with Linda-like operations, thus facilitating distributed data storage, access and management. Data are accessed in a uniform fashion, regardless if they are stored locally or remotely. Neither perpetual processes to maintain the distributed data nor establishing (sometimes very complex) of interprocess communication channels to broadcast messages or collect results is needed, in contrast to the traditional parallel logic programming approach. However, relevant parallel logic programming techniques cannot be discarded since our model does not offer enough synchronizing power for some classes of problems. The paper describes operations added to Strand and the semantics of the extended computational model. A comparison of standard logic programming techniques with the ones offered by our extended model is presented. Every example is followed by a detailed analysis. Finally, the implementation and performance issues are addressed.

An important limitation of traditional logic programming as a knowledge representation tool, in comparison with classical logic, is that logic programming does not allow us to deal directly with incomplete information. In order to overcome this limitation, we extend the class of general logic programs by including classical negation, in addition to negation-as-failure. The semantics of such extended programs is based on the method of stable models. The concept of a disjunctive database can be extended in a similar way. We show that some facts of commonsense knowledge can be represented by logic programs and disjunctive databases more easily when classical negation is available. Computationally, classical negation can be eliminated from extended programs by a simple preprocessor. Extended programs are identical to a special case of default theories in the sense of Reiter.

One of the key characteristics of cellular Machine-to-Machine (M2M) communications is the generation of small data packets by a large number of devices. The excessive control signaling required for dynamic scheduling of such uplink data in the Long Term Evolution (LTE) standard impedes the deployment of LTE networks for M2M communications. Traditional Semi-persistent scheduling (SPS) reduces the load on control channel but it is currently only developed for constant data rate voice packets. For realistic M2M environments, non-deterministic data traffic needs to be served in a resource efficient way. In this paper, we propose a buffer based adaptive SPS scheme for the LTE uplink which does not require the control signaling overhead of dynamic scheduling, but offers the same flexibility of increasing or decreasing the number of allocated radio resource blocks according to the latest buffer status of the device. We examine the influence of SPS periodicity on quality of Service (QoS) satisfaction of delay constrained stochastic M2M traffic and compare the performance of the proposed adaptive SPS scheme with rigid fixed allocation SPS schemes for such traffic. We also demonstrate how the adaptive SPS scheme can be implemented in a resource efficient way to meet the QoS requirements of a large number of machine type nodes through data aggregation.

Service Supply Chain Management (SSCM) is an analogous systems approach that is especially suitable for delivering mobile services. In this paper, customer-supplier duality has been demonstrated precisely and different examples have been presented for different bidirectional levels of service supply chains. The particular elements and impacts of SSCM have been addressed and its differences from manufacturing SCM have been highlighted. This paper implies that SCM in services is more complicated than in manufacturing. The major elements of SSCM which differentiate it from manufacturing have been emphasised. SSCM could provide considerable benefits to service organisations, including flexibility in delivery, dynamic scheduling and process orientation.