1,720,969 research outputs found
A GPU implementation of the Factored Sparse Approximate Inverse preconditioner for the iterative solution of SPD linear systems
No full textA factored sparse approximate inverse preconditioned conjugate gradient solver on graphics processing units
Full text linkGraphics Processing Units (GPUs) exhibit significantly higher peak performance than conventional CPUs. However, in general only highly parallel algorithms can exploit their potential. In this scenario, the iterative solution to sparse linear systems of equations could be carried out quite efficiently on a GPU as it requires only matrix-by-vector products, dot products, and vector updates. However, to be really effective, any iterative solver needs to be properly preconditioned and this represents a major bottleneck for a successful GPU implementation. Due to its inherent parallelism, the factored sparse approximate inverse (FSAI) preconditioner represents an optimal candidate for the conjugate gradient-like solution of sparse linear systems. However, its GPU implementation requires a nontrivial recasting of multiple computational steps. We present our GPU version of the FSAI preconditioner along with a set of results that show how a noticeable speedup with respect to a highly tuned CPU counterpart is obtained
Multilevel parallelism for the exploration of large-scale graphs
No full textWe present the most recent release of our parallel implementation of the BFS and BC algorithms for the study of large scale graphs. Although our reference platform is a high-end cluster of new generation Nvidia GPUs and some of our optimisations are CUDA specific, most of our ideas can be applied to other platforms offering multiple levels of parallelism. We exploit multi level parallel processing through a hybrid programming paradigm that combines highly tuned CUDA kernels, for the computations performed by each node, and explicit data exchange through the Message Passing Interface (MPI), for the communications among nodes. The results of the numerical experiments show that the performance of our code is comparable or better with respect to other state-of-the-art solutions. For the BFS, for instance, we reach a peak performance of 200 Giga Teps on a single GPU and 5.5 Tera Teps on 1024 Pascal ..
Enhanced GPU-based distributed breadth first search
No full textThere is growing interest in studying large scale graphs having millions of vertices and billions of edges, up to the point that a specific benchmark, called Graph500, has been defined to measure the performance of graph algorithms on modern computing architectures. At first glance, Graphics Processing Units (GPUs) are not an ideal platform for the execution of graph algorithms that are characterized by low arithmetic intensity and irregular memory access patterns. For studying really large graphs, multiple GPUs are required to overcome the memory size limitations of a single GPU. In the present paper, we propose several techniques to minimize the communication among GPUs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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