There is a sparse number of credible source models available from largemagnitude past earthquakes. A stochastic source-model-generation algorithm thus becomes necessary for robust risk quantification using scenario earthquakes. We present an algorithm that combines the physics of fault ruptures as imaged in laboratory earthquakes with stress estimates on the fault constrained by field observations to generate stochastic source models for large-magnitude (Mw 6.0-8.0) strike-slip earthquakes. The algorithm is validated through a statistical comparison of synthetic groundmotion histories from a stochastically generated source model for a magnitude 7.90 earthquake and a kinematic finite-source inversion of an equivalent magnitude past earthquake on a geometrically similar fault. The synthetic dataset comprises threecomponent ground-motion waveforms, computed at 636 sites in southern California, for 10 hypothetical rupture scenarios (five hypocenters, each with two rupture directions) on the southern San Andreas fault. A similar validation exercise is conducted for a magnitude 6.0 earthquake, the lower magnitude limit for the algorithm. Additionally, ground motions from the Mw 7.9 earthquake simulations are compared against predictions by the Campbell-Bozorgnia Next Generation Attenuation relation, as well as the ShakeOut scenario earthquake. The algorithm is then applied to generate 50 source models for a hypothetical magnitude 7.9 earthquake originating at Parkfield, California, with rupture propagating from north to south (toward Wrightwood), similar to the 1857 Fort Tejon earthquake. Using the spectral element method, three-component ground-motion waveforms are computed in the Los Angeles basin for each scenario earthquake and the sensitivity of ground-shaking intensity to seismic source parameters (such as the percentage of asperity area relative to the fault area, rupture speed, and rise time) is studied.