  In this article, we introduce, a novel open-source framework designed for
efficient parallel computation of projections in tomography leveraging either
multiple CPU cores or GPUs. This framework efficiently implements forward and
back projection functions for both sinogram and listmode data, utilizing
Joseph's method, which is further extended to encompass time-of-flight (TOF)
PET projections. Our evaluation involves a series of tests focusing on PET
image reconstruction using data sourced from a state-of-the-art clinical PET/CT
system. We thoroughly benchmark the performance of the projectors in non-TOF
and TOF, sinogram, and listmode employing multi CPU-cores, hybrid CPU/GPU, and
exclusive GPU mode. Moreover, we also investigate the timing of non-TOF
sinogram projections calculated in STIR (Software for Tomographic Image
Reconstruction) which recently integrated parallelproj as one of its projection
backends. Our results indicate that the exclusive GPU mode provides
acceleration factors between 25 and 68 relative to the multi-CPU-core mode.
Furthermore, we demonstrate that OSEM listmode reconstruction of
state-of-the-art real-world PET data sets is achievable within a few seconds
using a single consumer GPU.
