Abstract. To-date, the application of high-performance computing resources to Semantic Web data has largely focused on commodity hardware and distributed memory platforms. In this paper we make the case that more specialized hardware can offer superior scaling and close to an order of magnitude improvement in performance. In particular we examine the Cray XMT. Its key characteristics, a large, global sharedmemory, and processors with a memory-latency tolerant design, offer an environment conducive to programming for the Semantic Web and have engendered results that far surpass current state of the art. We examine three fundamental pieces requisite for a fully functioning semantic database: dictionary encoding, RDFS inference, and query processing. We show scaling up to 512 processors (the largest configuration we had available), and the ability to process 20 billion triples completely in-memory.
We present an approach for reducing redundancies in the design of reconfigurable antenna structures using graph models. The basics of graph models, their rules, and how they can be applied in the design of switch-based reconfigurable antennas are introduced. Based on these rules, a methodology is developed and formulated to reduce the number of switches and parts in the antenna structure, without sacrificing the desired antenna functions. This approach not only optimizes the overall structure of the antenna but it also reduces cost and overall losses. Several examples are presented and discussed to demonstrate the validity of this new approach through simulations and measurements that present good agreement.
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