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Tridiagonal system solver is an important kernel in many scientific and engineering applications. Even though quite a few parallel algorithms and implementations have been addressed in recent years, challenges still remain when solving large-scale tridiagonal system on heterogenous supercomputers. In this paper, a hierarchical algorithm framework SPIKE2 (pronounced 'SPIKE squared') is proposed to...
Considering the huge calculated amount of eigen-decomposition in one-dimensional Linear local tangent space alignment (LLTSA), this paper proposed a Semi-supervised two-dimensional manifold learning based on pair-wise constraints (2D-PCLTSA). 2D-PCLTSA adopts two-dimensional image matrices as the samples to extract image feature information, and uses pair-wise constraints as supervised information...
Cloud computing has become an increasingly popular service for data storage and processing. To keep users' data on the cloud from leaking to unauthorized users, probably including the cloud service providers, the data must be stored in an encrypted form. In the meantime, for data intended for sharing, an efficient access control must be provided. A common operation on the data is keyword search. Currently,...
Based on the former work about the variable upper limit integral of Bézier curve, the construction of the set made by all the degree n Bézier curves will be studied. Furthermore, for different degree n, the sets of the corresponding Bézier curves could build a construction like pyramid.
In this paper we propose the optimization of sparse matrix-vector multiplication (SpMV) with CUDA based on matrix bandwidth/profile reduction techniques. Computational time required to access dense vector is decoupled from SpMV computation. By reducing the matrix profile, the time required to access dense vector is reduced by 17% (for SP) and 24% (for DP). Reduced matrix bandwidth enables column index...
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