We present a multi-core reconfigurable quantum processor architecture, called Requp, which supports a hierarchical approach to mapping a quantum algorithm while sharing physical and logical ancilla qubits. Each core i...
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We present a multi-core reconfigurable quantum processor architecture, called Requp, which supports a hierarchical approach to mapping a quantum algorithm while sharing physical and logical ancilla qubits. Each core is capable of performing any quantum instruction. Moreover, we introduce a scalable quantum mapper, called Squash 2, which divides a given quantum circuit into a number of quantum modules each module is divided into k parts such that each part will run on one of k available cores. Experimental results demonstrate that Squash 2 can handle large-scale quantum algorithms while providing an effective mechanism for sharing ancilla qubits.
SimRank is an attractive link-based similarity measure used in fertile fields of Web search and sociometry. However, the existing deterministic method by Kusumoto et al. [24] for retrieving SimRank does not always pro...
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SimRank is an attractive link-based similarity measure used in fertile fields of Web search and sociometry. However, the existing deterministic method by Kusumoto et al. [24] for retrieving SimRank does not always produce high-quality similarity results, as it fails to accurately obtain diagonal correction matrix D. Moreover, SimRank has a "connectivity trait" problem: increasing the number of paths between a pair of nodes would decrease its similarity score. The best-known remedy, SimRank+ + [I], cannot completely fix this problem, since its score would still be zero if there are no common in-neighbors between two nodes. In this article, we study fast high-quality link-based similarity search on billion-scale graphs. (1) We first devise a "varied-D" method to accurately compute SimRank in linear memory. We also aggregate duplicate computations, which reduces the time of [24] from quadratic to linear in the number of iterations. (2) We propose a novel "cosine-based" SimRank model to circumvent the "connectivity trait" problem. (3) To substantially speed up the partial-pairs "cosine-based" SimRank search on large graphs, we devise an efficient dimensionality reduction algorithm, PSR#, with guaranteed accuracy. (4) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument in [24] that "if D is replaced by a scaled identity matrix (1 - gamma), their top-K rankings will not be affected much". (5) We propose a novel method that can accurately convert from Li et al. SimRank S to Jeh and Widom's SimRank S. (6) We propose GSR 5 , a generalisation of our "cosine-based" SimRank model, to quantify pairwise similarities across two distinct graphs, unlike SimRank that would assess nodes across two graphs as completely dissimilar. Extensive experiments on various datasets demonstrate the superiority of our proposed approaches in terms of high search quality, computational efficiency, accuracy, and scalability on billion-edge
In many applications using database systems, the conventional method of transaction processing can not be used. This is on account of lack of integration and existence of centralized solutions. Such situations exist w...
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In many applications using database systems, the conventional method of transaction processing can not be used. This is on account of lack of integration and existence of centralized solutions. Such situations exist within heterogeneous systems, mobile database transactions and time-critical applications requiring admission on priority for a select group of transactions. For example, in conventional methods, the deadlock detection is based on use of delay to cause and watch deadlocks. It generates many difficulties, such as, (a) high overheads of periodic checking (b) Non-deterministic nature of the delays, and (c) difficulties to scale-up the centralized solutions. The existing proposal lacks in local processing for distributed transactions. The proposed technique uses normal message communication among peers. The proposal leads to enhanced role for resource sites. The proposal introduces asynchronous operations in transaction processing. As a result the detection processes do not wait for occurrences of time-outs delays. In most cases the technique eliminates the possibility of occurrence of waiting delays.
Nowadays supercomputers have already entered in the petascale computing era and peak rate performance is dramatically increasing year after year. However, most of current algorithms are not capable of exploiting fully...
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Nowadays supercomputers have already entered in the petascale computing era and peak rate performance is dramatically increasing year after year. However, most of current algorithms are not capable of exploiting fully such a technology due to the well-known parallel programming related issues, such as synchronization, communication and fault tolerance. The aim of this paper is to present a probabilistic domain decomposition algorithm based on generating suitable random trees for solving nonlinear parabolic partial differential equations. These are of paramount importance since many important scientific and engineering problems are modeled by such type of differential equations. We stress that such algorithm is perfectly suited for both current and future high performance supercomputers, showing a remarkable performance and arbitrary scalability. While classical algorithms based on a deterministic domain decomposition exhibits strong limitations when increasing the size of the problem and the number of processors involved, probabilistic methods rather allow us to exploit efficiently massively parallel architectures, being the problem fully decoupled. Large-scale simulations runned on a high performance supercomputer confirm such properties.
Decentralized low-rank learning is an active research domain with extensive practical applications. A common approach to producing low-rank and robust estimations is to employ a combination of the nonsmooth quantile r...
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Decentralized low-rank learning is an active research domain with extensive practical applications. A common approach to producing low-rank and robust estimations is to employ a combination of the nonsmooth quantile regression loss and nuclear-norm regularizer. Nevertheless, directly applying existing techniques may result in slow convergence rates due to the doubly nonsmooth objective. To expedite the computation process, a decentralized surrogate matrix quantile regression method is proposed in this article. The proposed algorithm has a simple implementation and can provably converge at a linear rate. Additionally, we provide a statistical guarantee that our estimate can achieve an almost optimal convergence rate, regardless of the number of nodes. Numerical simulations confirm the efficacy of our approach.
Envision the situation that high quality information and entertainment is easily accessible to anyone, anywhere, at any time, and on any device. How realistic is this vision? And what does it require from the underlyi...
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ISBN:
(纸本)9780769518701
Envision the situation that high quality information and entertainment is easily accessible to anyone, anywhere, at any time, and on any device. How realistic is this vision? And what does it require from the underlying technology? Ambient Intelligence (AmI) integrates concepts ranging from ubiquitous computing to autonomous and intelligent systems. An AmI environment will be highly dynamic in many aspects. Underlying technology must be very flexible to cope with this dynamism. Scalability of technology is only one crucial aspect. This paper explores scalability from the processing, the communication, and the software perspectives.
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