The Densest Subgraph Problem (DSP) is widely used to identify community structures and patterns in networks such as bioinformatics and social networks. While solvable in polynomial time, traditional exact algorithms face computational and scalability limitations, leading to the adoption of faster, but non-optimal, heuristic methods. This work presents the first experimental study of the recently devised Incremental Parametric Cut (IPC) algorithm, which is an exact method for DSP and other "monotone ratio problems". Our findings demonstrate that IPC not only overcomes the limitations of previous exact approaches but also substantially outperforms leading state-of-the-art heuristics in both speed and solution quality. IPC's performance is also evaluated here for other "monotone ratio problems" related to conductance, Cheeger constant and normalized cut. For these, our experimental study on large-scale instances demonstrate exceptional computational speed. In particular, comparing IPC with the "fully parametric cut" algorithm, which is the only other efficient known optimization algorithm for such problems, demonstrate the superior performance of IPC. We provide here code and benchmarks, establishing IPC as a fast, scalable, and optimal solution framework for densest subgraph and related monotone ratio problems.