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Various algorithms have been proposed for finding a Bayesian network structure that is guaranteed to maximize a given scoring function. Implementations of state-of-the-art algorithms, solvers, for this Bayesian network structure learning problem rely on adaptive search strategies, such as branch-and-bound and integer linear programming techniques. Thus, the time requirements of the solvers are not...
We address the well-known score-based Bayesian network structure learning problem. Breadth-first branch and bound (BFBnB) has been shown to be an effective approach for solving this problem. Duplicate detection is an important component of the BFBnB algorithm. Previously, an external sorting-based technique was used for delayed duplicate detection (DDD). We propose a hashing-based technique for DDD...
The execution of computationally intensive parallel applications in heterogeneous environments, where the quality and quantity of computing resources available to a single user continuously change, often leads to irregular behavior, in general due to variations of algorithmic and systemic nature. To improve the performance of scientific applications, loop scheduling algorithms are often employed for...
In this paper, an artificial neural network (ANN) model is proposed to predict the flexibility (or robustness against system load fluctuations in heterogeneous computing systems) of dynamic loop scheduling (DLS) methods. The multilayer perceptron (MLP) ANN model has been used to predict the degree of robustness of a DLS method, given specific values for the problem size, the system size, and the characteristics...
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