Semidefinite Programming (SDP) is a powerful tool to obtain upper bounds for packing problems. For example, one can consider the kissing problem of the hemisphere in dimension 8 which asks for the maximal number of pairwise non-overlapping spheres which can simultaneously touch a central hemisphere in 8-dimensional Euclidean space. The E8 lattice gives a kissing configuration of 183 points. Moreover, using an SDP given by Bachoc and Vallentin one gets an upper bound of 182.99999999996523. Hence, the optimal value is 183. But how can we obtain the exact rational solution of the SDP based on the floating point results given by the SDP solver?