GRay: a Massively Parallel GPU-Based Code for Ray Tracing in Relativistic Spacetimes
Chi-kwan Chan (1, 2), Dimitrios Psaltis (1, 3), Feryal Ozel (1, and 3, 4) ((1) Department of Astronomy, University of Arizona, (2) Nordic, Institute for Theoretical Physics, (3) Institute for Theory, Computation,, Harvard-Smithsonian Center for Astrophysics

TL;DR
GRay is a GPU-accelerated, massively parallel code for efficient photon trajectory tracing in curved spacetimes, enabling advanced simulations and analysis of black hole environments and observational predictions.
Contribution
It introduces GRay, a novel GPU-based ray tracing integrator that significantly outperforms CPU methods and facilitates detailed modeling of relativistic phenomena around black holes.
Findings
GRay achieves over 300 GFLOP peak performance on a single GPU.
It is two orders of magnitude faster than existing CPU-based codes.
Provides accurate models of black hole shadows and photon rings.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
