جستجوی مقالات مرتبط با کلیدواژه « partitioning » در نشریات گروه « برق »

Nowadays, quantum computing has played a significant role in increasing the speed of algorithms. Due to the limitations in the manufacturing technologies of quantum computers, the design of a large-scale quantum computer faces many challenges. One solution to overcome these challenges is the design of distributed quantum systems. In these systems, quantum computers are connected to each other through the teleportation protocol to transfer quantum information. Since quantum teleportation requires quantum resources, it is necessary to reduce the number of that. The purpose of this paper is to present a distributed quantum system considering the two goals of balanced distribution of qubits and minimizing the number of teleportation protocols in two levels. In the first level, by presenting a dynamic programming algorithm, an attempt has been made to distribute qubits in a balanced manner and reduce the number of connections between subsystems. According to the output partitioning obtained from the first level, in the second level and in the stage of implementation of global gates, when one of the qubits of this gate is teleported from the home to the desired destination, this qubit may be able to be used by a number of global gates, observing the precedence restrictions and as a result it reduces the number of teleportations. The obtained results show the better performance of the proposed algorithm.

Block-centric graph processing systems have received significant attention in recent years. To produce the required partitions, most of these systems use general-purpose partitioning methods. As a result, the performance of them has been limited. To face this problem, special partitioning algorithms have been proposed by researchers. However, these methods focused on traditional partitioning measures like the number of cutting-edges and the load-balance. In return, the power of block-centric graph processing systems is due to unique characteristics that are focused on the design of them. According to basic and important characteristics of these systems, in this paper two new measures are proposed as partitioning goals. To the best of our knowledge, the proposed method is the first work that considers the diameter and size of the high-level graph as optimization factors for partitioning purposes. The evaluation of the proposed method over real graphs showed that we could significantly reduce the diameter of the high-level graph. Moreover, the number of cutting-edges of the proposed method are very close to Metis, one of most popular centralized partitioning methods. Since the number of required supersteps in block-centric graph processing systems mainly depends on the diameter of the high-level graph, the proposed method can significantly improve the performance of these systems.

A real-time data warehouse is a collection of recent and hierarchical data that is used for managers’ decision-making by creating online analytical queries. The volume of data collected from data sources and entered into the real-time data warehouse is constantly increasing. Moreover, as the volume of input data to the real time data warehouse increases, the interference between online loading operations and online analytical processing increases. These two stated challenges have become the most important issues regarding real time data warehouse. In this article, a method is presented to improve the analytical queries response time in the real time data warehouse architecture using materialized views concatenation. This process takes place by: (1) storing the results of performed queries in each real time section, (2) transferring the results to the next section when transferring data to the section. Each real time section contains data of its previous section, which have been transferred in several stages. As a result, the calculated results of the queries are also transmitted by transferring data, and consequently, for achieving desired outcome, the previously calculated results can be combined without the need to run the queries again. The proposed method has reduced both analytical queries response time and data entry interference caused by the simultaneous and long-term execution of queries. This study faces two challenges: (1) applying the proposed method to a small amount of data in the real time section and (2) the changes in the proposed method for applying to big data.

In this paper, a novel method has been proposed that, determines the necessary actions to restore the power system as quickly as possible in the event of an overall outage of the power system, when there is more than one black start unit in the power system, it will be divided into several islands in order to faster restoration. The proposed method uses a bi-level optimization algorithm that, determines needed actions for restoration process in an integrated approach. These actions are partitioning of the power system, units' start-up sequence, determination of optimal transmission path between buses and sequence of energizing transmission lines and buses. The proposed algorithm has been implemented on IEEE 39-bus test system to show the advantage of the method.

Computation offloading is known to be among the effective solutions of running heavy applications on smart mobile devices. However, irregular changes of a mobile data rate have direct impacts on code partitioning when offloading is in progress. It is believed that once a rate-adaptive partitioning performed, the replication of such substantial processes due to bandwidth fluctuation can be avoided. Currently, a wide range of mobile applications are based on web services, which in turn influences the process of offloading and partitioning. As a result, mobile users are prone to face difficulties in data communications due to cost of preferences or connection quality. Taking into account the fluctuations of mobile connection bandwidth and thereby data rate constraints, the current paper proposes a method of adaptive partitioning and computation offloading in three forms. Accordingly, an optimization problem is primarily formulated to each of three main objectives under the investigation. These objectives include run time, energy consumption and the weighted composition of run time and energy consumption. Next, taking into consideration the time complexity of the optimization problems, a heuristic partitioning method based on Genetic Algorithm (GABP) is proposed to solve each of the three objectives and with the capability of acceptable performance maintenance in both dynamic and static partitionings. In order to evaluate and analyze the performance of the proposed approach, a simulation framework was built to run for random graphs of different sizes with the capability of setting specific bandwidth limits as target. The simulation results evidence improved performance against bandwidth fluctuations when compared to similar approaches. Moreover, it was also seen that once the problem circumstances are modified, the offloading can take place in the vicinity of the target node. Furthermore, we implemented the proposed method in form of an application on Android platform to conduct experiments on real applications. The experiments prove that those partitions of the applications requiring higher processing reqources rather than data rate are the best candidates for offloading.

So far, several methods have been utilized for truss optimization where metaheuristic thechniqes have been used as one of the most common methods for such purpose. These thechniqes have received much attention due to their independence from gradient information and efficient global search property. In some of the metaheuristic thechniqes, random generation of the preliminary population leads to creation of repetitive population, dependency of final optimum solution on preliminary population, increasing of the number of analyses and reduction of the algorithm efficiency. To overcome these disadvantages, the present study employs some intelligent thechniqes for the generation and the selection of the population. So, a new metaheuristic algorithm called SPR is proposed for discrete truss optimization problems. The SPR algorithm is based on the sensitivity of the elements, the partitioning of the search space and the rebirthing of the optimization procedure. The structural members which are more sensitive to changing the cross-sectional areas are recognized through the procedure. In order to find the best approximate solution for any design variable, the search space is discritized to some partitions. Rebirthing technique searches for better optimum solutions around the last local optimum point for any design variable to end the stagnation states. The performance of the proposed algorithm is investigated using several benchmark size optimization problems of truss structures from the literature. In comparison to the available results in the literature, the proposed SPR algorithm showed capable of obtaining the optimum design with much lesser computational attempt.