Abstract
Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances.
Highlights
Mathematical models have grown in size and complexity becoming often computationally intractable
From the results we identified, we considered only those satisfying these criteria: (1) the source was available as a set of MATLAB scripts, (2) it was of a significant dimension, (3) the code ran as-is on MATLAB
We summarize the solutions available to execute MATLAB-only model representations and the performance gain of 16 real-world mathematical models. These models cannot be handled by SBML solutions, while the topology optimization[10] works only in QSPcc, other than MATLAB
Summary
Mathematical models have grown in size and complexity becoming often computationally intractable. Finding solutions to the initial value problem (IVP) is a task that, apart for a limited number of cases, must be performed numerically[4,6,7] To this aim, different ODE integration algorithms have been developed. High-level and easy-to-use interpreted languages such as MATLAB8 and R9 tend to be slower in terms of execution time than lower-level and compiled languages such as C and Fortran The former usually offer a more user-friendly environment for the development of models and are more familiar among scientists working in different research fields, while the latter requires programming skills that are usually beyond the average scientist’s knowledge and are prone to diverting the research efforts from the modeling activity to coding and debugging. We contribute a compilerbased solution, namely QSPcc, delivering flexibility in the variety of handled models, ease-of-use in the limited or not-required adaptations to existing code and performance speedup in large scale projects
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