Doc: Illustrate options for potentially speeding up model import/simulation - #1965
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FFroehlich
approved these changes
Jan 30, 2023
| "\n", | ||
| "#### Parameters as constants\n", | ||
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| "By default, AMICI will generate sensitivity equations with respect to all model parameters. If it is clear upfront, that sensitivities with respect to certain parameters will not be required, their IDs can be passed to [amici.sbml_import.SbmlImporter.sbml2amici](https://amici.readthedocs.io/en/latest/generated/amici.sbml_import.SbmlImporter.html#amici.sbml_import.SbmlImporter.sbml2amici) or [amici.pysb_import.pysb2amici](https://amici.readthedocs.io/en/latest/generated/amici.pysb_import.html?highlight=pysb2amici#amici.pysb_import.pysb2amici) via the `constant_parameters` to not generate the respective equations. This will reduce CPU time and RAM requirements during import and simulation.\n", |
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constant_parameters argument
| "#### Parameters as constants\n", | ||
| "\n", | ||
| "By default, AMICI will generate sensitivity equations with respect to all model parameters. If it is clear upfront, that sensitivities with respect to certain parameters will not be required, their IDs can be passed to [amici.sbml_import.SbmlImporter.sbml2amici](https://amici.readthedocs.io/en/latest/generated/amici.sbml_import.SbmlImporter.html#amici.sbml_import.SbmlImporter.sbml2amici) or [amici.pysb_import.pysb2amici](https://amici.readthedocs.io/en/latest/generated/amici.pysb_import.html?highlight=pysb2amici#amici.pysb_import.pysb2amici) via the `constant_parameters` to not generate the respective equations. This will reduce CPU time and RAM requirements during import and simulation.\n", | ||
| "The PEtab import will take care of that automatically.\n", |
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will automatically pass all parameters with petab.ESTIMATE==False as constant_parameters arguments.
| "#### Extracting common subexpressions\n", | ||
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| "For some models, the size of the generated model code can be significantly reduced by extracting common subexpressions. This can yield substantial reductions of compile times and RAM-requirements. Very large models might not compile without this option. Extracting common subexpressions can be enabled by setting an environment variable `AMICI_EXTRACT_CSE=1` before model import.\n", | ||
| "The downside is, that the generated model code becomes rather unreadable. The increase in import time when enabling this feature is usually <15%, the effect on code size and compile time is highly model dependent. Mostly models with tightly coupled ODEs, as obtained from complex rate laws or spatial discretizations of ODEs, seem to benefit. For models with mass action kinetics, this option seems to not be helpful and rather increases compile time (e.g., for FröhlichGer2022, the compile time doubles).\n", |
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FröhlichGer2022 is a pysb model, which internally uses expressions/rate laws to avoid reuse of common expressions. Technically also not mass action kinetics due to the thermodynamic model formulation, although the resulting equations are effectively the same.
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| "#### Parallelization\n", | ||
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| "For large models or complex model expressions, symbolic computation of the derivatives can be quite time consuming. This can be parallelized by setting the environment variable `AMICI_IMPORT_NPROCS` to the number of parallel processes that should be used. The impact strongly depends on the model. Note that setting this value too may have a negative performance impact (benchmark!).\n", |
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| "For large models or complex model expressions, symbolic computation of the derivatives can be quite time consuming. This can be parallelized by setting the environment variable `AMICI_IMPORT_NPROCS` to the number of parallel processes that should be used. The impact strongly depends on the model. Note that setting this value too may have a negative performance impact (benchmark!).\n", | |
| "For large models or complex model expressions, symbolic computation of the derivatives can be quite time consuming. This can be parallelized by setting the environment variable `AMICI_IMPORT_NPROCS` to the number of parallel processes that should be used. The impact strongly depends on the model. Note that setting this value for smaller model may have a negative performance impact (benchmark!).\n", |
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| "If only the objective function gradient is required, adjoint sensitivity analysis are often preferable over forward sensitivity analysis. As a rule of thumb, adjoint sensitivity analysis seem to outperform forward sensitivity analysis for models with more than 20 parameters:\n", | ||
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| "\n", |
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looks like we're missing a \n here, not sure why though.
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