Abstract
We present a Resource-Aware Model-Based Optimization framework RAMBO that leads to efficient utilization of parallel computer architectures through resource-aware scheduling strategies. Conventional MBO fits a regression model on the set of already evaluated configurations and their observed performances to guide the search. Due to its inherent sequential nature, an efficient parallel variant can not directly be derived, as only the most promising configuration w.r.t. an infill criterion is evaluated in each iteration. This issue has been addressed by generalized infill criteria in order to propose multiple points simultaneously for parallel execution in each sequential step. However, these extensions in general neglect systematic runtime differences in the configuration space which often leads to underutilized systems. We estimate runtimes using an additional surrogate model to improve the scheduling and demonstrate that our framework approach already yields improved resource utilization on two exemplary classification tasks.
J. Richter and H. Kotthaus—These authors are contributed equally.
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Notes
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Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., Casalicchio, G., Jones, Z.M.: mlr: machine learning in R. J. Mach. Learn. Res. 17(170), 1–5 (2016). http://jmlr.org/papers/v17/15-066.html
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Bischl et al., mlrMBO: Model-Based Optimization for mlr. https://github.com/berndbischl/mlrMBO.
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Acknowledgments
This work was partly supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876, A3.
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Richter, J., Kotthaus, H., Bischl, B., Marwedel, P., Rahnenführer, J., Lang, M. (2016). Faster Model-Based Optimization Through Resource-Aware Scheduling Strategies. In: Festa, P., Sellmann, M., Vanschoren, J. (eds) Learning and Intelligent Optimization. LION 2016. Lecture Notes in Computer Science(), vol 10079. Springer, Cham. https://doi.org/10.1007/978-3-319-50349-3_22
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