Polygon overlay is one of the complex operations in Geographic Information Systems (GIS). In GIS, a typical polygon tends to be large in size often consisting of thousands of vertices. Sequential algorithms for this p...
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ISBN:
(纸本)9781479913725
Polygon overlay is one of the complex operations in Geographic Information Systems (GIS). In GIS, a typical polygon tends to be large in size often consisting of thousands of vertices. Sequential algorithms for this problem are in abundance in literature and most of the parallel algorithms concentrate on parallelizing edge intersection phase only. Our research aims to develop parallel algorithms to find overlay for two input polygons which can be extended to handle multiple polygons and implement it on General Purpose Graphics Processing Units (GPGPU) which offers massive parallelism at relatively low cost. Moreover, spatial data files tend to be large in size (in GBs) and the underlying overlay computation is highly irregular and compute intensive. MapReduce paradigm is now standard in industry and academia for processing large-scale data. Motivated by MapReduce programming model, we propose to develop and implement scalable distributed algorithms to solve large-scale overlay processing in this dissertation.
Computing the single-source shortest path (SSSP) is one of the fundamental graph algorithms, and is used in many applications. Here, we focus on computing SSSP on large dynamic graphs, i.e. graphs whose structure evol...
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ISBN:
(纸本)9781538683873;9781538683866
Computing the single-source shortest path (SSSP) is one of the fundamental graph algorithms, and is used in many applications. Here, we focus on computing SSSP on large dynamic graphs, i.e. graphs whose structure evolves with time. We posit that instead of recomputing the SSSP for each set of changes on the dynamic graphs, it is more efficient to update the results based only on the region of change. To this end, we present a novel two-step shared-memory algorithm for updating SSSP on weighted large-scale graphs. The key idea of our algorithm is to identify changes, such as vertex/edge addition and deletion, that affect the shortest path computations and update only the parts of the graphs affected by the change. We provide the proof of correctness of our proposed algorithm. Our experiments on real and synthetic networks demonstrate that our algorithm is as much as 4X faster compared to computing SSSP with Galois, a state-of-the-art parallel graph analysis software for shared memory architectures. We also demonstrate how increasing the asynchrony can lead to even faster updates. To the best of our knowledge, this is one of the first practical parallel algorithms for updating networks on shared-memory systems, that is also scalable to large networks.
Computational efficient evaluation of penalized estimators of multivariate exponential family distributions is sought. These distributions encompass amongst others Markov random fields with variates of mixed type (e.g...
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Frequent itemsets mining (FIM) plays an important role in many data mining areas. With the explosion of data scale, a number of parallel FIM algorithms have been proposed. Although existing solutions have outstanding ...
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ISBN:
(纸本)9781538614839
Frequent itemsets mining (FIM) plays an important role in many data mining areas. With the explosion of data scale, a number of parallel FIM algorithms have been proposed. Although existing solutions have outstanding scalability, they suffer from high consumption of CPU and memory for recursively mining frequent itemsets based on a tree-structure. In this paper, we propose a novel parallel algorithm, named PNPFI. It employs three novel key optimizations. In detail, the itemsets are stored by the N-list structure, which is more compact than existing tree-based structure. It uses a new structure, called P-Subsume, to generate some frequent itemsets without the process of N-list intersection. In addition, PNPFI proposes a new load balancing strategy, which intelligently divides a large-scale FIM problem into a set of tasks based on the profiled load of each item. Compared with the state-of-the-art algorithms, experimental results show that PNPFI gets a performance improvement of 39% on average (max to 79%), and reduces the memory usage by 58% on average (max to 90%).
Interprocessor communication often dominates the runtime of large matrix computations. We present a parallel algorithm for computing QR decompositions whose bandwidth cost (communication volume) can be decreased at th...
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We present original time-parallel algorithms for the solution of the implicit Euler discretization of general linear parabolic evolution equations with time-dependent self-adjoint spatial operators. Motivated by the i...
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The Ray-Casting algorithm is an important method for fast real-time surface display from 3D medical images. Based on Ray-Casting algorithm, a novel parallel Ray-Casting algorithm is proposed in this paper. A novel ope...
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—This paper presents an efficient technique for matrix-vector and vector-transpose-matrix multiplication in distributed-memory parallel computing environments, where the matrices are unstructured, sparse, and have a ...
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Game-theoretical approach to the analysis of parallel algorithms is proposed. The approach is based on presentation of the parallel computing as a congestion game. In the game processes compete for resources such as c...
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Private Information Retrieval (PIR) enables the data owners to share and/or retrieve data on remote repositories without leaking any information as to which a data item is requested. Although it is always possible to ...
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Private Information Retrieval (PIR) enables the data owners to share and/or retrieve data on remote repositories without leaking any information as to which a data item is requested. Although it is always possible to download the entire dataset, this is clearly a waste of bandwidth. A fundamental approach in the literature for PIR is exploiting homomorphic cryptosystems. In these approaches, not one but many modular exponentiations need to be computed and multiplied to obtain the desired result. This multi-exponentiation operation can be implemented by exponentiating the bases to their corresponding exponents one-by-one. However, when the operation is considered as a whole, it can be performed in a more efficient way. Although individual exponentiations are pleasingly parallelizable, the combined multi-exponentiation requires a careful parallel implementation. In this work, we propose a generic tensor-based PIR scheme and efficient and novel techniques to parallelize multi-exponentiations on multicore processors with perfect load balance. The experimental results show that our load balancing techniques make a parallel multi-exponentiation up to %27 faster when the size of the bases and the exponents are 4096 bits and the number of threads is 16.
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