Rediscovering Chaos? Analysis of GPU Computing Effects in Graph-coupled NeuralODEs
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One of the fundamental guidelines in research is to publish reproducible results. Without this core assumption, conclusions and decisions cannot be trusted. Also in the last years, GPU computing became the defacto default choice of computing platform. This paper studies the confounding effects of utilizing GPU accelerations, computing with finite precision and modeling chaotic systems. Especially when it comes to end-to-end learned NeuralODE models, the forward as well as the backward pass dynamics need to be stable and reliable reproducible. In this work, we investigate the phenomenon of non-reproducible computations despite fixing random seeds and data batches and reason how to retain the sweet spot of computing highly efficient, modeling complex dynamics and being deterministically reproducible.