We introduce GRay, a massively parallel integrator designed to trace the
trajectories of billions of photons in a curved spacetime. This GPU-based
integrator employs the stream processing paradigm, is implemented in CUDA
C/C++, and runs on nVidia graphics cards. The peak performance of GRay using
single precision floating-point arithmetic on a single GPU exceeds 300 GFLOP
(or 1 nanosecond per photon per time step). For a realistic problem, where the
peak performance cannot be reached, GRay is two orders of magnitude faster than
existing CPU-based ray tracing codes. This performance enhancement allows more
effective searches of large parameter spaces when comparing theoretical
predictions of images, spectra, and lightcurves from the vicinities of compact
objects to observations. GRay can also perform on-the-fly ray tracing within
general relativistic magnetohydrodynamic algorithms that simulate accretion
flows around compact objects. Making use of this algorithm, we calculate the
properties of the shadows of Kerr black holes and the photon rings that
surround them. We also provide accurate fitting formulae of their dependencies
on black hole spin and observer inclination, which can be used to interpret
upcoming observations of the black holes at the center of the Milky Way, as
well as M87, with the Event Horizon Telescope.

介绍了一种基于 GPU 的并行积分器 GRay，它能够在弯曲空间中追踪数十亿光子的轨迹，并且其单 GPU 峰值性能超过 300 GFLOP，可用于黑洞精确成像，提供黑洞旋转和观察者倾角的精确拟合公式，可以用于解释事件视界望远镜的数据。