Programming complex embedded systems involves reasoning through intricate system interactions along lengthy paths between sensors, actuators, and control processors. This is a challenging, time-consuming, and error-pr...
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Programming complex embedded systems involves reasoning through intricate system interactions along lengthy paths between sensors, actuators, and control processors. This is a challenging, time-consuming, and error-prone process requiring significant interaction between engineers and software programmers. Furthermore, the resulting code generally lacks. modularity and robustness in the presence of failure. model-based programming addresses these limitations, allowing engineers to program reactive systems by specifying high-level control strategies and by assembling commonsense models of the system hardware and software. In executing a control strategy, model-based executives reason about the models "on the fly, " to track system state, diagnose faults, and perform reconfigurations. This paper develops the Reactive model-based Programming Language (RMPL) and its executive, called Titan. RMPL provides the features of synchronous, reactive languages, with the added ability of reading and writing to state variables that are hidden within the physical plant being controlled. Titan executes an RMPL program using extensive component-based declarative models of the plant to track states, analyze anomalous situations, and generate novel control sequences. Within its reactive control loop, Titan employs propositional inference to deduce the system's current and desired states, and it employs model-based reactive planning to move the plant from the current to the desired state.
In simulations running in parallel, the processors would have to synchronize with other processors to maintain correct global order of computations. This can be done either by blocking computation until correct order ...
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In simulations running in parallel, the processors would have to synchronize with other processors to maintain correct global order of computations. This can be done either by blocking computation until correct order is guaranteed, or by speculatively proceeding with the best guess (based on local information) and later correcting errors if/as necessary. Since the gainful lengths of speculative forays depend on the dynamics of the application software and hardware at runtime, an online control system is necessary to dynamically choose and/or switch between the blocking and speculative strategies. In this paper, we formulate the reversible speculative computing in large-scale parallel computing as a dynamic linear feedback control (optimization) system model and evaluate its performance in terms of time and cost savings as compared to the traditional (forward) computing. We illustrate with an exact analogy in the form of vehicular travel under dynamic, delayed route information. The objective is to assist in making the optimal decision on what computational approach is to be chosen, by predicting the amount of time and cost savings (or losing) under different environments represented by different parameters and probability distribution functions. We consider the cases of Gaussian, exponential and log-normal distribution functions. The control system is intended for incorporating into speculative parallel applications such as optimistic parallel discrete event simulations to decide at runtime when and to what extent speculative execution can be performed gainfully.
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