  Computed Tomography (CT) is a widely used technology that requires
compute-intense algorithms for image reconstruction. We propose a novel
back-projection algorithm that reduces the projection computation cost to 1/6
of the standard algorithm. We also propose an efficient implementation that
takes advantage of the heterogeneity of GPU-accelerated systems by overlapping
the filtering and back-projection stages on CPUs and GPUs, respectively.
Finally, we propose a distributed framework for high-resolution image
reconstruction on state-of-the-art GPU-accelerated supercomputers. The
framework relies on an elaborate interleave of MPI collective communication
steps to achieve scalable communication. Evaluation on a single Tesla V100 GPU
demonstrates that our back-projection kernel performs up to 1.6x faster than
the standard FDK implementation. We also demonstrate the scalability and
instantaneous CT capability of the distributed framework by using up to 2,048
V100 GPUs to solve 4K and 8K problems within 30 seconds and 2 minutes,
respectively (including I/O).
