Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications

Many scientific problems require multiple distinct computational tasks to be executed in order to achieve a desired solution. We introduce the Ensemble Toolkit (EnTK) to address the challenges of scale, diversity and reliability they pose. We describe the design and implementation of EnTK, characterize its performance and integrate it with two distinct exemplar use cases: seismic inversion and adaptive analog ensembles. We perform nine experiments, characterizing EnTK overheads, strong and weak scalability, and the performance of two use case implementations, at scale and on production infrastructures. We show how EnTK meets the following general requirements: (i) implementing dedicated abstractions to support the description and execution of ensemble applications; (ii) support for execution on heterogeneous computing infrastructures; (iii) efficient scalability up to O(10^4) tasks; and (iv) fault tolerance. We discuss novel computational capabilities that EnTK enables and the scientific advantages arising thereof. We propose EnTK as an important addition to the suite of tools in support of production scientific computing

{2-[6-(1H-Benzimidazol-2-yl-κN 3)-2-pyridyl-κN]benzimidazolato-κN}(dicyanamido-κN)(methanol-κO)copper(II)

In the title compound, [Cu(C19H12N5)(C2N3)(CH3OH)], the CuII atom is coordinated by three N atoms from an anionic 2,6-bis­(1H-benzimidazol-2-yl)pyridine (bbp) ligand, an O atom from a methanol mol­ecule and one N atom from a dicyanamide anion. The crystal structure is stabilized by O—H⋯N and N—H⋯N hydrogen bonds, forming a three-dimensional network

A Novel Web Page Filtering System by Combining Texts and Images

With the rapid development of the Internet, people benefit much from the sharing of information. Meanwhile, the WWW era is a double-edged sword which spreads harmful and erotic content widely. In this paper, a new statistical approach has been exploited by combining the results of two or more different classification methods using our filtering system. We first briefly introduce the classification of discrete texts, continuous texts and images separately, and then describe the specific way we have been exploring to merge the text and image classification result. Also there is a section illustrating our system framework. Finally we assess our method by demon-strating the experimental results and comparing it to some common-used filtering methods