1. Parallelization: Yes, they do parallelize! Conceptually, it is fairly easy to use accurate algorithms locally, then reduce the local accumulators. A side effect of doing this is cutting (or reducing) the noise in the output introduced by the parallelization (note that this is not to be confused with deterministic summation/BLAS, which enforces the order of operations to achieve the same side effect but do not improve accuracy).

2. It varies wildly depending on the algorithm you are using, the implementation of said algorithm, and the operations you are performing outside of the summation (if you sum the results of a loop performing non-trivial operations, that can easily overlap with the summation overhead making it essentially free). The only rule of thumb I can give is that runtime cost increases with desired accuracy. My recommendation would be to switch from a naive sum to using an exact accumulator and observe the difference in output value (letting you know how impactful numerical errors in that sum were) and runtime (letting you know how expensive the switch is for you), then dial it down if needed.