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Institute
With parallelism becoming the standard in computer design, research on parallel constraint solving technique is of vital importance for enhancing the performance of constraint solving. In this dissertation, we reviewed the literature on exploiting parallelism in constraint solving to help gain insight into the rationale of different types of parallel constraint solving approaches. On this basis, we analyzed the effectiveness of parallel constraint solving, with the focus on obtaining a first solution when solving computationally hard combinatorial problems. We have shown that a well-designed search space splitting method and constraint programming model can enable the embarrassingly parallel search (EPS) to solve some open instances of the social golfer problem that have not been solved by a sequential algorithm. We also observed superlinear speedups when solving these instances, which confirms our theoretical analysis. Besides, we examined two practical constraint optimization problems, including the traveling tournament problem with predefined venues and the talent scheduling problem. Our proposed constraint models outperformed the existing models on the same instances, and the EPS approach could always attain better feasible solutions in terms of the optimal objective value by using more parallel processors. To explore the use of massively parallel processing, we proposed the parallel stochastic portfolio search, which is a simple and non-intrusive way to parallelize different incarnations of a sequential solver. When comparing the existing portfolio to our portfolio approach by solving the same constraint satisfaction problems using the same constraint models, our technique could solve harder and larger instances. The successes of our new parallel approaches are attributed to early diversity; i.e., some diversity early in the search introduced by parallelism can offset early mistakes caused by weak heuristic choices. Unlike the other techniques (e.g., limited discrepancy search) used to overcome early mistakes, the studied two parallel constraint solving approaches not only can explore more nodes simultaneously but also does not sacrifice the guarantee of completeness.
We also presented a hypertree decomposition method that builds a degenerate decomposition tree for a given constraint network, in which each node of the decomposition tree possesses and executes a subset of constraints of the given constraint network. The usefulness of our proposed parallel techniquedepends on whether we can find an efficient way to join the results of each node.
Verification of software for Contiki-based low-power embedded systems using software model checking
(2017)
The main building blocks for the internet of things are connected embedded systems. Often these systems are also used in safety critical applications. Therefore, it is particularly important that these devices work according to their specification i.e. they behave as intended. Nowadays, even for simple devices embedded operating systems as Contiki are used to simplify application development and to increase portability between different hardware platforms.
The main objective of this thesis is to present a methodology for the verification of software applications written for the operation system Contiki, taking the system hardware into account. Therefore, software model checking and especially bounded model checking [BCC⁺03] is used as a technique, which allows to formally verify software for embedded systems.
For verifying the software against its specification, it is also necessary to build a model of the system hardware. Thereby, the difficulty is to create a model which is detailed enough to capture the hardware behavior so that the software performs correctly, while keeping the computation effort for the verification process manageable. In this work, the drivers which communicate with the hardware are therefore replaced with abstract models during the verification process. This enables the verification based on an abstract hardware platform independent of specific hardware.
A special role within embedded systems play interrupts. Interrupts are used to save power and can also be used to react on external events. Current methods for verification of interrupt driven software are based on the interleaving model and partial order reduction to reduce the size of the verification problem. This thesis argues that this method is not sufficient for software, whose behavior relies on periodically occurring interrupts. Therefore, in this thesis, a new approach called periodic interrupt modeling is introduced. This approach can be applied automatically and reduces the number of incorrect verification results due to inaccurate modeling. In addition, properties can be proven that depend on the number of occurring interrupts.
Using applications for the Contiki operating system, and based on a verification flow, the approaches toward interrupt modeling are compared.
Functional Reactive Programming (FRP) is a declarative approach to programming reactive and interactive systems. We develop a common interface and a common semantics for FRP and discuss existing implementation approaches. Afterwards, we develop a technique for ensuring start time consistency as well as an efficient implementation of discrete signals. Finally, we present a novel record system, which makes FRP more usable in practice. Our system offers generic record combinators that are statically typed.
With parallelism becoming the standard in computer design, research on parallel constraint solving technique is of vital importance for enhancing the performance of constraint solving. In this dissertation, we reviewed the literature on exploiting parallelism in constraint solving to help gain insight into the rationale of different types of parallel constraint solving approaches. On this basis, we analyzed the effectiveness of parallel constraint solving, with the focus on obtaining a first solution when solving computationally hard combinatorial problems. We have shown that a well-designed search space splitting method and constraint programming model can enable the embarrassingly parallel search (EPS) to solve some open instances of the social golfer problem that have not been solved by a sequential algorithm. We also observed superlinear speedups when solving these instances, which confirms our theoretical analysis. Besides, we examined two practical constraint optimization problems, including the traveling tournament problem with predefined venues and the talent scheduling problem. Our proposed constraint models outperformed the existing models on the same instances, and the EPS approach could always attain better feasible solutions in terms of the optimal objective value by using more parallel processors. To explore the use of massively parallel processing, we proposed the parallel stochastic portfolio search, which is a simple and non-intrusive way to parallelize different incarnations of a sequential solver. When comparing the existing portfolio to our portfolio approach by solving the same constraint satisfaction problems using the same constraint models, our technique could solve harder and larger instances. The successes of our new parallel approaches are attributed to early diversity; i.e., some diversity early in the search introduced by parallelism can offset early mistakes caused by weak heuristic choices. Unlike the other techniques (e.g., limited discrepancy search) used to overcome early mistakes, the studied two parallel constraint solving approaches not only can explore more nodes simultaneously but also does not sacrifice the guarantee of completeness.
We also presented a hypertree decomposition method that builds a degenerate decomposition tree for a given constraint network, in which each node of the decomposition tree possesses and executes a subset of constraints of the given constraint network. The usefulness of our proposed parallel techniquedepends on whether we can find an efficient way to join the results of each node.