论文标题
用B型拼图模仿宇宙学生长函数
Emulating cosmological growth functions with B-Splines
论文作者
论文摘要
鉴于GPU加速度，诸如求解普通微分方程之类的顺序操作可以是梯度评估的瓶颈，并阻碍潜在的速度增长。在这项工作中，我们将重点放在宇宙粒子网格模拟中的增长函数及其时间导数上，并表明这些是使用基于梯度的推理算法时的大多数时间成本。我们建议构建新型的条件B型模拟器，该模拟器直接学习生长因子作为时间的函数，以宇宙学为条件。我们证明，这些模拟器足够准确，以免我们的结果偏向宇宙论的推断，并且可能会导致及时的数量级增长，尤其是对于中间至中等大小的模拟。
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.
