Current simulation-sampling techniques construct accurate model state for each measurement by continuously warming large microarchitectural structures (e.g., caches and the branch predictor) while functionally simulating the billions of instructions between measurements. This approach, called functional warming, is the main performance bottleneck of simulation sampling and requires hours of runtime while the detailed simulation of the sample requires only minutes. Existing simulators can avoid functional simulation by jumping directly to particular instruction stream locations with architectural state checkpoints. To replace functional warming, these checkpoints must additionally provide microarchitectural model state that is accurate and reusable across experiments while meeting tight storage constraints. In this paper, we present a simulation-sampling framework that replaces functional warming with live-points without sacrificing accuracy. A live-point stores the bare minimum of functionally-warmed state for accurate simulation of a limited execution window while placing minimal restrictions on microarchitectural configuration. Live-points can be processed in random rather than program order, allowing simulation results and their statistical confidence to be reported while simulations are in progress. Our framework matches the accuracy of prior simulation-sampling techniques (i.e., ±3% error with 99.7% confidence), while estimating the performance of an 8-way out-of-order superscalar processor running SPEC CPU2000 in 91 seconds per benchmark, on average, using a 12 GB live-point library