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QSPcc reduces bottlenecks in computational model simulations
Danilo Tomasoni1, Alessio Paris1, Stefano Giampiccolo1
1Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
Communications Biology
|September 2, 2021
Summary
This study introduces QSPcc, a compiler that translates complex mathematical models into fast C code. This approach significantly accelerates scientific research, making computationally intractable models feasible for analysis and optimization.
Area of Science:
- Computational biology
- Scientific computing
- Pharmacology
Background:
- Mathematical models in science are increasingly complex and computationally intensive.
- Traditional scientific programming languages like MATLAB and R can create performance bottlenecks during model analysis.
- Sensitivity analysis and optimization require numerous model runs, exacerbating performance issues.
Purpose of the Study:
- To present a universal compiler-based approach for translating diverse scientific models into efficient C code.
- To demonstrate the capability of this approach in handling computationally intractable models, specifically in Quantitative Systems Pharmacology (QSP).
- To accelerate Research and Development (R&D) efforts across various natural science disciplines.
Main Methods:
- Development of a compiler (QSPcc) designed for universal application across engineering and life sciences modeling.
- Automatic translation of mathematical models into optimized C code.
- Benchmarking QSPcc against eight alternative solutions using 24 real-world projects.
Main Results:
- QSPcc enables research on previously intractable Quantitative Systems Pharmacology models, such as those for rare Lysosomal Storage Disorders.
- Achieved peak speed-ups of 22,000x and an arithmetic mean speed-up of 1,605x across diverse scientific fields.
- Demonstrated consistent superior performance compared to existing solutions.
Conclusions:
- The compiler-based approach offers a significant performance enhancement for scientific modeling.
- QSPcc effectively addresses computational challenges in complex modeling, enabling new research avenues.
- This technology accelerates R&D in natural sciences by overcoming performance limitations of traditional tools.
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