Model for an Intelligent Operating System for Executing Tasks on a Reconfigurable Parallel Architecture

Parallel processing is one approach to achieve the large computational processing capabilities required by many real-time computing tasks. One of the problems that must be addressed in the use of reconfigurable multiprocessor systems is matching the architecture configuration to the algorithms to be executed. This paper presents a conceptual model that explores the potential of artificial intelligence tools, specifically expert systems, to design an Intelligent Operating System for multiprocessor systems. The target task is the implementation of image understanding systems on multiprocessor architectures. PASM is used as an example multiprocessor. The Intelligent Operating System concepts developed here could also be used to address other problems requiring real-time processing. An example image understanding task is presented to illustrate the concept of intelligent scheduling by the Intelligent Operating System. Also considered is the use of the conceptual model when developing an image understanding system in order to test different strategies for choosing algorithms, imposing execution order constraints, and integrating results from various algorithms

Predictable Implementation of Real-Time Applications on Multiprocessor Systems on Chip

Worst-case execution time (WCET) analysis and, in general, the predictability of real-time applications implemented on multiprocessor systems has been addressed only in very restrictive and particular contexts. One important aspect that makes the analysis difficult is the estimation of the system\u27s communication behavior. The traffic on the bus does not solely originate from data transfers due to data dependencies between tasks, but is also affected by memory transfers as result of cache misses. As opposed to the analysis performed for a single processor system, where the cache miss penalty is constant, in a multiprocessor system each cache miss has a variable penalty, depending on the bus contention. This affects the tasks\u27 WCET which, however, is needed in order to perform system scheduling. At the same time, the WCET depends on the system schedule due to the bus interference. In this context, we present an approach to worst-case execution time analysis and system scheduling for real-time applications implemented on multiprocessor SoC architectures. We will also address the bus scheduling policy and its optimization, which are of huge importance for the performance of such predictable multiprocessor applications

Hard real-time performances in multiprocessor-embedded systems using ASMP-Linux

Multiprocessor systems, especially those based on multicore or multithreaded processors, and new operating system architectures can satisfy the ever increasing computational requirements of embedded systems.ASMP-LINUX is a modified, high responsiveness, open-source hard real-time operating system for multiprocessorsystems capable of providing high real-time performance while maintaining the code simple and not impacting on theperformances of the rest of the system. Moreover, ASMP-LINUX does not require code changing or application recompiling/relinking.In order to assess the performances of ASMP-LINUX, benchmarks have been performed on several hardware platformsand configurations

A multiprocessor based packet-switch: performance analysis of the communication infrastructure

The intra-chip communication infrastructures are receiving always more attention since they are becoming a crucial part in the development of current SoCs. Due to the high availability of pre-characterized hard-IP, the complexity of the design is moving toward global interconnections which are introducing always more constraints at each technology node. Power consumption, timing closure, bandwidth requirements, time to market, are some of the factors that are leading to the proposal of new solutions for next generation multi-million SoCs. The need of high programmable systems and the high gate-count availability is moving always more attention on multiprocessors systems (MP-SoC) and so an adequate solution must be found for the communication infrastructure. One of the most promising technologies is the Network-On-Chip (NoC) architecture, which seems to better fit with the new demanding complexity of such systems. Before starting to develop new solutions, it is crucial to fully understand if and when current bus architectures introduce strong limitations in the development of high speed systems. This article describes a case study of a multiprocessor based ethernet packet-switch application with a shared-bus communication infrastructure. This system aims to depict all the bottlenecks which a shared-bus introduces under heavy load. What emerges from this analysis is that, as expected, a shared-bus is not scalable and it strongly limits whole system performances. These results strengthen the hypothesis that new communication architectures (like the NoC) must be found

The multiprocessor real-time scheduling of general task systems

The recent emergence of multicore and related technologies in many commercial systems has increased the prevalence of multiprocessor architectures. Contemporaneously, real-time applications have become more complex and sophisticated in their behavior and interaction. Inevitably, these complex real-time applications will be deployed upon these multiprocessor platforms and require temporal analysis techniques to verify their correctness. However, most prior research in multiprocessor real-time scheduling has addressed the temporal analysis only of Liu and Layland task systems. The goal of this dissertation is to extend real-time scheduling theory for multiprocessor systems by developing temporal analysis techniques for more general task models such as the sporadic task model, the generalized multiframe task model, and the recurring real-time task model. The thesis of this dissertation is: Optimal online multiprocessor real-time scheduling algorithms for sporadic and more general task systems are impossible; however, efficient, online scheduling algorithms and associated feasibility and schedulability tests, with provably bounded deviation from any optimal test, exist. To support our thesis, this dissertation develops feasibility and schedulability tests for various multiprocessor scheduling paradigms. We consider three classes of multiprocessor scheduling based on whether a real-time job may migrate between processors: full-migration, restricted-migration, and partitioned. For all general task systems, we obtain feasibility tests for arbitrary real-time instances under the full-and restricted-migration paradigms. Despite the existence of tests for feasibility, we show that optimal online scheduling of sporadic and more general systems is impossible. Therefore, we focus on scheduling algorithms that have constant-factor approximation ratios in terms of an analysis technique known as resource augmentation. We develop schedulability tests for scheduling algorithms, earliest-deadline-first (edf) and deadline-monotonic (dm), under full-migration and partitioned scheduling paradigms. Feasibility and schedulability tests presented in this dissertation use the workload metrics of demand-based load and maximum job density and have provably bounded deviation from optimal in terms of resource augmentation. We show the demand-based load and maximum job density metrics may be exactly computed in pseudo-polynomial time for general task systems and approximated in polynomial time for sporadic task systems

Predictability of Fixed-Job Priority Schedulers on Heterogeneous Multiprocessor Real-Time Systems

The multiprocessor Fixed-Job Priority (FJP) scheduling of real-time systems is studied. An important property for the schedulability analysis, the predictability (regardless to the execution times), is studied for heterogeneous multiprocessor platforms. Our main contribution is to show that any FJP schedulers are predictable on unrelated platforms. A convenient consequence is the fact that any FJP schedulers are predictable on uniform multiprocessors

Radiation safety based on the sky shine effect in reactor

In the reactor operation, neutrons and gamma rays are the most dominant radiation. As protection, lead and concrete shields are built around the reactor. However, the radiation can penetrate the water shielding inside the reactor pool. This incident leads to the occurrence of sky shine where a physical phenomenon of nuclear radiation sources was transmitted panoramic that extends to the environment. The effect of this phenomenon is caused by the fallout radiation into the surrounding area which causes the radiation dose to increase. High doses of exposure cause a person to have stochastic effects or deterministic effects. Therefore, this study was conducted to measure the radiation dose from sky shine effect that scattered around the reactor at different distances and different height above the reactor platform. In this paper, the analysis of the radiation dose of sky shine effect was measured using the experimental metho

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited