Nonlinear least-squares regression is a valuable tool for gaining chemical insights into complex systems. Yet, the success of nonlinear regression as measured by residual sum of squares (RSS), correlation, and reproducibility of fit parameters strongly depends on the availability of a good initial solution. Without such, iterative algorithms quickly become trapped in an unfavorable local RSS-minimum. For determining an initial solution, a high-dimensional parameter space needs to be screened, a process that is very time-consuming but can be parallelized. Another advantage of parallelization is equally important: After determining initial solutions, the used processors can be tasked to each optimize an initial guess. Even if several of these optimizations become stuck in a shallow local RSS-minimum, other processors continue and improve the regression outcome. A software package for parallel processing-based constrained nonlinear regression (RegressionLab) has been developed, implemented, and tested on a variety of hardware configurations. As proof-of-principle, microalgae to environment interactions have been studied by infrared attenuated total reflection spectroscopy. Additionally, light microscopy has been used to monitor cell production. It is shown that spectroscopic data sets with 10,000s of data points and >1000 nonlinear model parameters as well as imaging data with 100,000s of data points and >2000 nonlinear model parameters may now be investigated by constrained nonlinear regression. Acceleration factors of up to 8.1 have been obtained which is of high practical relevance when computations take weeks on single-processor machines. Solely using parallel processing, the RSS values may be improved up to a factor of 5.5.