  Gridding operation, which is to map non-uniform data samples onto a uniformly
distributedgrid, is one of the key steps in radio astronomical data reduction
process. One of the mainbottlenecks of gridding is the poor computing
performance, and a typical solution for suchperformance issue is the
implementation of multi-core CPU platforms. Although such amethod could usually
achieve good results, in many cases, the performance of gridding is
stillrestricted to an extent due to the limitations of CPU, since the main
workload of gridding isa combination of a large number of single instruction,
multi-data-stream operations, which ismore suitable for GPU, rather than CPU
implementations. To meet the challenge of massivedata gridding for the modern
large single-dish radio telescopes, e.g., the Five-hundred-meterAperture
Spherical radio Telescope (FAST), inspired by existing multi-core CPU
griddingalgorithms such as Cygrid, here we present an easy-to-install,
high-performance, and open-source convolutional gridding framework, HCGrid,in
CPU-GPU heterogeneous platforms. Itoptimises data search by employing
multi-threading on CPU, and accelerates the convolutionprocess by utilising
massive parallelisation of GPU. In order to make HCGrid a more
adaptivesolution, we also propose the strategies of thread organisation and
coarsening, as well as optimalparameter settings under various GPU
architectures. A thorough analysis of computing timeand performance gain with
several GPU parallel optimisation strategies show that it can leadto excellent
performance in hybrid computing environments.
