• BaseBatchScripts A generally functional version of the shell scripts used initiate the pyABC based parameter inference on an HPC. When executed, it starts a Redis-server, connects workers to the server and runs the python program containing the inference assignments. Originally provided by Emad Alamoudi.

• Models Contains all files required to run the models, evaluate their results and create the figures included in the body of this thesis. Also contains the results of the runs we performed, however mostly without the database in which the full run details are saved, as these files are too large (usually several hundred MB for each run). The databases remain saved on the cluster infrastructure on which the respective test was run and can be made available upon request.

  • M1_Tumor Everything required to run and evaluate the tests of the tumor growth model (M1). The inference is performed in the python programs TumorAdaptiveEps.py and TumorListEps.py with an adaptive and a static epsilon respectively. Executing these files returns detailed information about each run in a database file, and some additional statistics about the effect of the look-ahead scheduling is written into .csv file.

    • BatchScript A version of the shell scripts tailored to the tumor model test runs. The main changes are a different worker call, necessary to prevent daemonic processes, and the amount of active nodes being handed to the python program call as an argument.

    • Figures The jupyter notebooks load the run data in order to visualize the results as seen in Figures 17-22 (created in TumorVisualization.py) and Figure 23 (created in TumorVisStat.py). The figures are the ones summarizing the results of the runs with a static epsilon schedule on equal conditions (see Section [sec:TumorRT]).

    • Testresults The results of the performed (M1) runs, sorted by population size and workers used. Does not include the databases themselves, but instead some extracted information summarizing each generation, the .csv files and all visualizations for each individual run.

  • M2_HIV The python programs used to perform parameter inference for the HIV model (M2) and the results of the 8 runs (.csv files, and for the LA, adaptive epsilon run on 256 workers also the database). Also, the notebook used to visualize these results, creating Figures 25, 26. Model and posterior plots (Figure 24) provided by Nils Bundgaard.

  • T1_ODE Everything required to run and evaluate the tests of the ODE model (T1). The inference is performed multiple times by executing the python program ODEWLogfiles.py. Executing this file returns detailed information about each repetition in a separate database file, some additional statistics about the effect of the look-ahead scheduling for each run in different .csv files and additionally a .txt file containing summarized wall time statistics.

    • BatchScript A version of the shell scripts tailored to the ODE model test runs. Mainly features some additional arguments being passed between the shell scripts.

    • Figures The python program (ODEBoxPlotCreation.py) reads in all databases and .csv files to plot the summarizing graphs showing equivalence of the posteriors as seen in Figures 3-5. The jupyter notebook creates the wall time comparison plots (Figures 6-10) from the wall time summaries in the .txt files.

    • Testresults The summarized wall time results of the test sorted by the runtime variance and the amount of nodes. Databases and .csv logfiles remain on the server, as there are several hundred of them.

  • T2_MJP Everything required to run and evaluate the tests of the Gillespie algorithm based model (T2). Basically a copy of the files in T1_ODE, only with adapted paths and the model in the test file (MJPRuntimeTest.py) changed to the one for (M2). See above for details about the sub-folders.

  • T3_UnbModes Files used to run model (T3) both locally (UnbalancedModes.ipynb) and with minor modifications on an HPC (UnbalancedModes.py). Additionally the returned databases of the HPC runs (once using DYN, once LA scheduling) and the visualizations of the local and the cluster runs.

• Other Figures The remaining Figures used in the main body (Figure 1,2 and the sketch of the tumor model). Including the program used to create Figure [fig:strategies], which reads in a file with a list of accepted or rejected particles with their simulation times and imitates the an STAT, DYN and LA scheduling approach to show how these particles would be distributed on the workers.

• pyABC Two versions of the pyABC package: Version 0.10.14 which was used for testing and a modified version, including some changes to the built-in visualization functions and a first implementation of some of the Enhancements mentioned in Section [sec:OPTESS].

• tumor2d Version 1.0.0 of the tumor2d package required to run the tumor model (M1).