Measures CPU single-thread performance by simulating logistic population growth (the Verhulst model) over 8 million timesteps using the Euler method. Lower time is better.
The logistic growth model, proposed by Pierre François Verhulst in 1838, describes how populations grow in an environment with limited resources. Unlike simple exponential growth, the logistic equation includes a carrying capacity K — the maximum sustainable population size. As the population approaches K, growth slows and eventually stops, producing the characteristic S-shaped sigmoid curve found in systems from bacterial colonies to national censuses.
The governing equation is dN/dt = r·N·(1 − N/K), where N is the population, r is the intrinsic growth rate, and K is the carrying capacity. This nonlinear ODE has an exact analytical solution, but numerical integration allows study of modified variants, stochastic perturbations, and coupled multi-species systems that lack closed-form answers. It also forms the single-species limit of the full Lotka-Volterra competition and SIR epidemiological models.
This benchmark integrates the Verhulst equation over 8 million timesteps using the forward Euler method — the simplest first-order numerical integrator. Each step requires one floating-point multiply, one divide, and two additions. Because all 8 million steps are sequential and data-dependent, the workload is a clean single-thread CPU throughput test: the JavaScript engine cannot parallelize it and memory access patterns are trivial. A lower elapsed time indicates faster CPU execution.