The sparse grid combination technique can be used to mitigate the curse of dimensionality and to gain insight into the physics of hot fusion plasmas with the gyrokinetic code GENE. With the sparse grid combination technique, massively parallel simulations can be performed on target resolutions that would be prohibitively large for standard full grid simulations. This can be achieved by numerically decoupling the target simulation into several smaller ones. Their time dependent evolution requires load balancing to obtain near optimal scaling beyond the scaling capabilities of GENE itself. This approach requires that good estimates for the runtimes exist.
This paper revisits this topic for large-scale nonlinear global simulations and investigates common machine learning techniques, such as support vector regression and neural networks. It is shown that, provided enough data can be collected, load modeling by data-driven techniques can outperform expert knowledge-based fits – the current state-of-the-art approach.
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