Kai J. Kohlhoff

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Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle(More)
MOTIVATION Data clustering techniques are an essential component of a good data analysis toolbox. Many current bioinformatics applications are inherently compute-intense and work with very large datasets. Sequential algorithms are inadequate for providing the necessary performance. For this reason, we have created Clustering Algorithms for Massively(More)
Scientific discovery is transitioning from a focus on data collection to an emphasis on analysis and prediction using large-scale computation. With appropriate software support, scientists can do these computations with unused cycles in commercial clouds. Moving science into the cloud will promote data sharing and collaborations that will accelerate(More)
We present an implementation of parallel K-means clustering, called Kps-means, that achieves high performance with near-full occupancy compute kernels without imposing limits on the number of dimensions and data points permitted as input, thus combining flexibility with high degrees of parallelism and efficiency. As a key element to performance improvement,(More)
In the version of this Article originally published, Figure 4 displayed incorrectly drawn chemical structures for five of the ligands. The correct structures were, however, used in the calculations. The hemiaminal group previously depicted in compounds 2–4 should have been a β-amino alcohol, compound 7 contained an extra benzylic carbon and compound 8 had(More)
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