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Accelerating Numerical Simulations of Supernovae with GPUs

机译:用GPU加速超新佳素的数值模拟

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To understand the mechanism of supernova explosions, large-scale numerical simulations are essential because of their complex dynamics described by a coupled equations of neutrino radiation transport and hydrodynamics of dense matter. In this work, we employ GPUs to accelerate such simulations. By adopting the implicit scheme for the evolution equation, an iterative linear equation solver for the coefficient matrix is the most time consuming part, which has been shown to be efficiently offloaded to GPUs. There are still several secondary bottlenecks which cost substantial time in the simulations, such as computation of the collision term of the Boltzmann equation of neutrinos, and parameter tuning of the matrices in the iterative solver. This paper focuses on these parts and offloads them to GPUs by employing CUDA in the case of spherically symmetric system. As a result, the time evolution is sufficiently accelerated for desirable model sizes toward systematic survey of stellar models with better grid resolution than that adopted so far.
机译:为了理解超新星爆炸的机理,大规模的数值模拟,因为通过中微子辐射传输的耦合方程和的致密物质流体力学描述其复杂的动力学是必不可少的。在这项工作中,我们采用的GPU来加速这种模拟。通过采用隐式的演化方程,迭代线性方程解算器的系数矩阵是最耗时的部分,它已被证明可以有效地卸载到图形处理器。仍有花费大量的时间在模拟几个次级瓶颈,如中微子的波尔兹曼方程的碰撞项的计算,并且在迭代求解器矩阵的参数调谐。本文采用CUDA的球对称系统的情况下,专注于这些部件和卸载他们的GPU。其结果是,随时间的变化充分加速了向恒星模型具有更好的网格分辨率比迄今采用的系统的调查可取的模型大小。

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