用GPU对吸积致密天体模型中光线跟踪光强和偏振的展望
Prospects for ray-tracing light intensity and polarization in models of accreting compact objects using a GPU
摘要：
事件视界望远镜（EHT）最近发布了两个超大质量黑洞吸积流的高分辨率图像。我们对这些图像的物理理解取决于致密物体周围等离子体和辐射数值模型的准确性和精度。这项工作的目标是加快辐射传输模拟，用于创建与EHT观测比较的黑洞模拟图像。将强引力下等离子体中广义相对论全极化辐射转移的射线追踪程序移植到图形处理器上。我们描述了我们的GPU实现和进行加速测试，使用模型的光学薄对流为主的吸积流（ADAF）到黑洞实现半解析和三维广义相对论磁流体力学模拟，低和非常高的图像像元分辨率，和两个不同的CPU+ GPU。我们表明，具有高精度计算能力的GPU可以显着减少图像生产的计算时间，加速因子高达约1200。显著的加速有助于，例如，动态模型拟合到EHT数据，包括极化数据。该方法的扩展可能使研究从等离子体发射与非热粒子分布函数的准确近似同步辐射率是不可用的。显著的加速将EHT图像库的生成的碳足迹减少了至少一个数量级。
英文摘要：
The Event Horizon Telescope (EHT) has recently released high-resolution images of accretion flows onto two supermassive black holes. Our physical understanding of these images depends on accuracy and precision of numerical models of plasma and radiation around compact objects. The goal of this work is to speed up radiative-transfer simulations used to create mock images of black holes for comparison with the EHT observations. A ray-tracing code for general relativistic and fully polarized radiative transfer through plasma in strong gravity is ported onto a graphics processing unit (GPU). We describe our GPU implementation and carry out speedup tests using models of optically thin advection-dominated accretion flow (ADAF) onto a black hole realised semi-analytically and in 3D general relativistic magnetohydrodynamics simulations, low and very high image pixel resolutions, and two different sets of CPU+GPUs. We show that a GPU with high double precision computing capability can significantly reduce the image production computational time, with a speedup factor of up to approximately 1200. The significant speedup facilitates, e.g., dynamic model fitting to the EHT data, including polarimetric data. The method extension may enable studies of emission from plasma with nonthermal particle distribution functions for which accurate approximate synchrotron emissivities are not available. The significant speedup reduces the carbon footprint of the generation of the EHT image libraries by at least an order of magnitude.
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