Nowadays, multi-core processor, a multi processor system on a single chip, is widely available, and the processing capacity of computer system is highly extended. However, parallelization of computer program at the thread level, namely multithreading, is required in order to shorten the execution time of (in other words, to speed up) the single program by sufficiently utilizing the power of the multi-core processor. In this paper, we propose a method of thread partitioning that partitions a single program code into parallel threads. The method intends for programs which is hardly sped up by conventional multithreading techniques, and the method is based on a speculative thread execution model. In the method, a program code is partitioned into threads based on the frequencies of the program execution path (the execution sequence of the program basic blocks). Partitioning of program code along the most frequently executed path and no data dependencies between partitioned threads allow each thread to execute in parallel and the program can be sped up. For the purpose of clarifing the performance achieved by our proposed method, the method is applied to the practical program code, that is difficult to speedup by utilizing the parallelism at loop level, and the cycle-level processor simulation is performed.
In this paper, we examine the behavior of evolutionary multiobjective optimization (EMO) algorithms to clarify the difficulties in their scalability to many-objective optimization problems. Whereas EMO algorithms usually work well on two-objective problems, it has also been reported that they do not work well on many-objective problems. First, we examine the behavior of the most well-known and frequently-used Pareto-based EMO algorithm (i. e. , NSGA-II) on many-objective 0/1 knapsack problems. Experimental results show that the search ability of NSGA-II is severely deteriorated by the increase in the number of objectives. This is because the selection pressure toward the Pareto front is severely weakened by the increase in the number of non-dominated solutions. Next we briefly review some approaches to the scalability improvement of EMO algorithms to many-objective problems. Then we examine their effects on the search ability of NSGA-II. Experimental results show that the improvement in the convergence of solutions to the Pareto front often leads to the decrease in their diversity.
Distinguishing different people with identical names is becoming more and more important in person searches on the Web. The aim of this research is to dispatch useful labels for identifying persons in “person clusters,” which are generated as a result of person searches on the Web. In this paper, we propose a method to label person clusters with “vocation-related information.” The vocation-related information includes broader terms that may be considered as vocations, and terms that are useful to infer vocations, not only those rigorously defined as vocations. Our method is based on (a) extracting candidates of vocation-related information by using HTML structures and simple heuristics, and (b) generating vocation-related information by using term frequencies,synonym clustering, and Web search engines. Experimental results revealed the usefulness of the proposed method.
In order to design tracking control systems for a class of systems with rapid or abrupt changes, it is effective in improving tracking performance to construct preview control systems considering future information of reference signals. In this paper we study the optimal tracking problems with preview for a class of linear continuous-time Markovian jump systems. Our systems are described by the switching systems with Markovian mode transition. The necessary and sufficient conditions for the solvability of our LQ tracking problem are given by coupled Riccati differential equations with terminal conditions. Correspondingly feedforward compensators introducing future information are given by coupled differential equations with terminal conditions. We consider three different tracking problems depending on the property of the reference signals. Finally we give numerical examples.