We study the hard-core model defined on independent sets of an input graph where the independent sets are weighted by a parameter $λ>0$. For constant $Δ$, previous work of Weitz (2006) established an FPTAS for the partition function for graphs of maximum degree $Δ$ when $λ< λ_c(Δ)$. The threshold $λ_c(Δ)$ is the critical point for the phase transition for uniqueness/non-uniqueness on the infinite $Δ$-regular trees. Sly (2010) showed that there is no FPRAS, unless NP=RP, when $λ>λ_c(Δ)$. The running time of Weitz's algorithm is exponential in $\log(Δ)$. Here we present an FPRAS for the partition function whose running time is $O^*(n^2)$. We analyze the simple single-site Glauber dynamics for sampling from the associated Gibbs distribution. We prove there exists a constant $Δ_0$ such that for all graphs with maximum degree $Δ\geqΔ_0$ and girth $\geq 7$, the mixing time of the Glauber dynamics is $O(n\log(n))$ when $λ<λ_c(Δ)$. Our work complements that of Weitz which applies for constant $Δ$ whereas our work applies for all $Δ\geq Δ_0$. We utilize loopy BP (belief propagation), a widely-used inference algorithm. A novel aspect of our work is using the principal eigenvector for the BP operator to design a distance function which contracts in expectation for pairs of states that behave like the BP fixed point. We also prove that the Glauber dynamics behaves locally like loopy BP. As a byproduct we obtain that the Glauber dynamics converges, after a short burn-in period, close to the BP fixed point, and this implies that the fixed point of loopy BP is a close approximation to the Gibbs distribution. Using these connections we establish that loopy BP quickly converges to the Gibbs distribution when the girth $\geq 6$ and $λ<λ_c(Δ)$.