CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

In this research, computationally demanding tasks such as model evaluation and
optimisation, also eminently time-consuming techniques such as bootstrapping, would
greatly benefit from the application of parallel programming. The analysis pipeline
that was presented in Section 2.2.4 involves several “embarrassingly parallel”
iterations as summarised in Figure 3-2. Since these tasks run independently of each
other, a divide-and-conquer parallelisation has been indeed feasible. There are several
communication mechanisms that allow computational tasks to run cooperatively in
parallel across a single multicore machine. In this work, the Message Passing
Interface (MPI) (Gabriel et al., 2004) via the Rmpi (Yu, 2011) interface is employed.
Figure 3-2 Embarrassingly parallel problems in the analysis pipeline
The figure demonstrates all the iterations running in sequence within the analysis pipeline. Each of
these problems runs independently, thus it can be easily parallelised.
3.2.2 Approximation Algorithms
Based on the overall accuracies of Figure 2-6, the ensembles of RBF SVMs were
noticeably the most promising classification method. The generalisation performance
of a nonlinear (RBF) SVM is greatly dependent on a suitable selection of and .
Therefore, a two-step grid-search approach was initially applied as a means of
hyperparameter optimisation (see Section 2.2.4). A grid-search over an arbitrary range
of parameter values is an intuitive but computationally intensive technique, to the
point that even parallel programming proved to be inadequate. In addition, the
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