The last decade has seen an increasing demand from the industrial field of computerized visual inspection. Applications rapidly become more complex and often with more demanding real time constraints. However, from 2004 onwards the clock frequency of CPUs has not increased significantly. Computer Vision applications have an increasing demand for more processing power but are limited by the performance capabilities of sequential processor architectures. The only way to get more performance using commodity hardware, like multi-core processors and graphics cards, is to go for parallel programming. This article focuses on the practical question: How can the processing time for vision algorithms be improved, by parallelization, in an economical way and execute them on multiple platforms?
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To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.
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Inset plots can be used to “zoom in” on densely populated areas of a graph or to add extra relevant data in the form of, for example, distribution plots. However, the standard Stata command for combining plots, graph combine, does not permit this type of seamless integration. Each plot within a graph combine object is allocated a grid cell that cannot be placed within another grid cell— at least not without certain (invariably unwanted) graphical complications. We present a fairly simple work-around to this issue using reproducible examples. The main idea is to plot insets along a second axis and then artificially modify the range of this axis to constrain the inset plot within a specified area of the main graph. Additional tips are included for producing more intricate, multilayered inset graphs.
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