In the light of GPU accelerations, sequential operations such as solving ordinary differential equations can be bottlenecks for gradient evaluations and hinder potential speed gains. In this work, we focus on growth functions and their time derivatives in cosmological particle mesh simulations and show that these are the majority time cost when using gradient based inference algorithms. We propose to construct novel conditional B-spline emulators which directly learn an interpolating function for the growth factor as a function of time, conditioned on the cosmology. We demonstrate that these emulators are sufficiently accurate to not bias our results for cosmological inference and can lead to over an order of magnitude gains in time, especially for small to intermediate size simulations.
Ngai Pok Kwan (The Chinese University of Hong Kong)
Chirag Modi (Flatiron Institute)
Yin Li (Flatiron Institute)
Shirley Ho (Flatiron Institute)
More from the Same Authors
2022 : Towards a non-Gaussian Generative Model of large-scale Reionization Maps »
Yu-Heng Lin · Sultan Hassan · Bruno Régaldo-Saint Blancard · Michael Eickenberg · Chirag Modi