Terms Of Total Computation Time Research Articles

Fast Connected Components Computation in Large Graphs by Vertex Pruning

Finding connected components is a fundamental task in applications dealing with graph analytics, such as social network analysis, web graph mining and image processing. The exponentially growing size of today's graphs has required the definition of new computational models and algorithms for their efficient processing on highly distributed architectures. In this paper we present cracker , an efficient iterative MapReduce-like algorithm to detect connected components in large graphs. The strategy of cracker is to transform the input graph in a set of trees, one for each connected component in the graph. Nodes are iteratively removed from the graph and added to the trees, reducing the amount of computation at each iteration. We prove the correctness of the algorithm, evaluate its computational cost and provide an extensive experimental evaluation considering a wide variety of synthetic and real-world graphs. The experimental results show that cracker consistently outperforms state-of-the-art approaches both in terms of total computation time and volume of messages exchanged.

A scalable optimization approach for fitting canonical tensor decompositions

AbstractTensor decompositions are higher‐order analogues of matrix decompositions and have proven to be powerful tools for data analysis. In particular, we are interested in the canonical tensor decomposition, otherwise known as CANDECOMP/PARAFAC (CP), which expresses a tensor as the sum of component rank‐one tensors and is used in a multitude of applications such as chemometrics, signal processing, neuroscience and web analysis. The task of computing CP, however, can be difficult. The typical approach is based on alternating least‐squares (ALS) optimization, but it is not accurate in the case of overfactoring. High accuracy can be obtained by using nonlinear least‐squares (NLS) methods; the disadvantage is that NLS methods are much slower than ALS. In this paper, we propose the use of gradient‐based optimization methods. We discuss the mathematical calculation of the derivatives and show that they can be computed efficiently, at the same cost as one iteration of ALS. Computational experiments demonstrate that the gradient‐based optimization methods are more accurate than ALS and faster than NLS in terms of total computation time. Copyright © 2011 John Wiley & Sons, Ltd.

Partitioning Medical Image Databases for Content-based Queries on a Grid

In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Grids are promising for content-based image retrieval in medical databases.

Intermediate-level feature extraction in novel parallel environments

Parallel systems provide an approach to robust computing. The motivation for this work arises from using modern parallel environments in intermediate-level feature extraction. This study presents parallel algorithms for the Hough transform (HT) and the randomized Hough transform (RHT). The algorithms are analyzed in two parallel environments: multiprocessor computers and workstation networks. The results suggest that both environments are suitable for the parallelization of HT. Because scalability of the parallel RHT is weaker than with HT, only the multiprocessor environment is suitable. The limited scalability forces us to use adaptive techniques to obtain good results regardless of the number of processors. Despite the fact that the speedups with HT are greater than with RHT, in terms of total computation time, the new parallel RHT algorithm outperforms the parallel HT.

Comparison of local and global computation and its implications for the role of optical interconnections in future nanoelectronic systems

Various methods of simulating diffusion phenomena with parallel hardware are discussed. In particular methods are compared requiring local and global communication among the processors in terms of total computation time. Systolic convolution on a locally connected array is seen to exhibit an asymptotic advantage over Fourier methods on a globally connected array. Whereas this may translate into a numerical advantage for extremely large numbers of ultrafast devices for two-dimensional systems, this is unlikely for three-dimensional systems. Thus global Fourier methods will be advantageous for three-dimensional systems for foreseeable device speeds and system sizes. The fact that optical interconnections are potentially advantageous for implementing the longer connections of such globally connected systems suggests that they can be beneficially employed in future nanoelectronic computers. Heat removal considerations play an important role in our conclusions.