QSPcc reduces bottlenecks in computational model simulations

Mathematical models have grown in size and complexity to the extent that are often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, models of ever-increasing complexity may be run thousands of times with varying parameters. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here is where QSPcc comes into play to seamlessly convert freely-written MATLAB mathematical models into fast C code. Contrary to existing solutions, no tweaking or dialect learning is required as full, complete Matlab projects can be ingested and seamlessly compiled by QSPcc. With speed-ups of 22000x peak, and 1605x arithmetic mean on the original modeling projects, our results show that QSPcc consistently delivers superior performances. Basic translation to R code is also provided to support interoperability between teams and across institutions either commercial, academic or philanthropic. In modern model-based drug discovery and development, QSPcc accelerated, or enabled, to holistically track sophisticated system interactions to support decision making in translational research of rare diseases. A docker version with no installation requirements, except Docker, is on DockerHub. Full article QSPcc reduces bottlenecks in computational model simulation.