  Cross-matching operation, which is to find corresponding data for the same
celestial object or region from multiple catalogues,is indispensable to
astronomical data analysis and research. Due to the large amount of
astronomical catalogues generated by the ongoing and next-generation
large-scale sky surveys, the time complexity of the cross-matching is
increasing dramatically. Heterogeneous computing environments provide a
theoretical possibility to accelerate the cross-matching, but the performance
advantages of heterogeneous computing resources have not been fully utilized.
To meet the challenge of cross-matching for substantial increasing amount of
astronomical observation data, this paper proposes
Heterogeneous-computing-enabled Large Catalogue Cross-matcher (HLC2), a
high-performance cross-matching framework based on spherical position deviation
on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible
cross-matching and can be directly applied to the fusion of large astronomical
cataloguesfrom survey missions and astronomical data centres. A performance
estimation model is proposed to locate the performance bottlenecks and guide
the optimizations. A two-level partitioning strategy is designed to generate an
optimized data placement according to the positions of celestial objects to
increase throughput. To make HLC2 a more adaptive solution, the
architecture-aware task splitting, thread parallelization, and concurrent
scheduling strategies are designed and integrated. Moreover, a novel
quad-direction strategy is proposed for the boundary problem to effectively
balance performance and completeness. We have experimentally evaluated HLC2
using public released catalogue data. Experiments demonstrate that HLC2 scales
well on different sizes of catalogues and the cross-matching speed is
significantly improved compared to the state-of-the-art cross-matchers.
