Are algorithms rated on the big-o notation affected by parallelism?

If quantum computing comes to something practical some day, then yes, complexity of algorithms will change.

In the meantime, parallelizing an algorithm, with a fixed number of processors, just divides its runtime proportionally (and that, in the best case, when there are no dependencies between the tasks performed at every processor). That means, dividing the runtime by a constant, and so the complexity remains the same.

Parallel execution doesn't change the basics of the complexity for a particular algorithm -- at best, you're just taking the time for some given size, and dividing by the number of cores. This may reduce time for a given size by a constant factor, but has no effect on the algorithm's complexity.

At the same time, parallel execution does sometimes change which algorithm(s) you want to use for particular tasks. Some algorithms that work well in serial code just don't split up into parallel tasks very well. Others that have higher complexity might be faster for practical-sized problems because they run better in parallel.

For an extremely large number of cores, the complexity of the calculation itself may become secondary to simply getting the necessary data to/from all the cores to do the calculation. most computations of big-O don't take these effects into account for a serial calculation, but it can become quite important for parallel calculations, especially for some models of parallel machines that don't give uniform access to all nodes.