We ported on the GPU with CUDA the Gaia Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver. The code aims to find the astrometric parameters of ~10^8 stars in the Milky Way, the attitude and the instrumental settings of the Gaia satellite, and the global parameter of the PPN formalism, by solving a system of linear equations, × = , with the LSQR iterative algorithm. The coefficient matrix is large, having ~10^11 × 10^8 elements, and sparse. The CUDA code accelerates ≳ 14 times compared to the current version of the AVU-GSR code, parallelized on the CPU with MPI+OpenMP and in production since 2014. This acceleration factor is ~9.2 times larger than the one obtained with a preliminary GPU porting with OpenACC, equal to ~1.5. We obtained this result by running the codes on the CINECA SuperComputer Marconi100, that has 4 NVIDIA Volta V100 GPUs per node, where the MPI+CUDA application has been recently put in production. This analysis represents a first step to understand the exascale behaviour of a class of applications that follow the same structure of this code, employed in several contexts. In the next months, we plan to run this code on the pre-exascale platform Leonardo of CINECA, with 4 next-generation A100 GPUs per node, to better investigate this behaviour. Computing on highly parallel devices, such as GPUs, might imply a consistent power saving, which might go towards the achievement of a Green Computing milestone
