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In our implementation, the prediction task is parallelized on GPU.
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The implementation of the prediction model is also discussed.
In this section, the concept and the implementation of the prediction algorithm is discussed.
The required components to conduct this experiment are: a benchmark to generate the workload patterns, an infrastructure to deploy the benchmark, and an implementation of the prediction algorithms.
The implementation of the prediction and configuration technique covers different parts – off-line and on-line components: The pattern determination and the construction of the patterns-quality matrix is done off-line based on monitoring logs.
An online implementation of the prediction method presented in this article is available at http://calpain.org.org
Availability: A Java implementation of the prediction methods and supplementary material are available at http://www.bio.ifi.lmu.de/Complexes/Substructures/.
We are currently planning to use this implementation for the prediction of targets for differentially expressed microRNAs in children affected with Kawasaki disease and controls and have made our code and examples available so that others could use on their data as well.
In our implementation, the final prediction is a weighted sum of the two according to the training accuracy of the two local models.
The goal of many implementation theories is the prediction of outcomes [ 21], and this is a significant methodological and theoretical challenge [ 9, 22].
However, the implementation of prediction algorithms which are able to assist physicians in the diagnostic is a difficult task.
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