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341 lines
13 KiB
Markdown
341 lines
13 KiB
Markdown
# POET
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[POET](https://doi.org/10.5281/zenodo.4757913) is a coupled reactive
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transport simulator implementing a parallel architecture and a fast,
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original MPI-based Distributed Hash Table.
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## Parsed code documentiation
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A parsed version of POET's documentation can be found at [Gitlab
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pages](https://naaice.git-pages.gfz-potsdam.de/poet).
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## External Libraries
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The following external libraries are shipped with POET:
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- **CLI11** - <https://github.com/CLIUtils/CLI11>
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- **IPhreeqc** with patches from GFZ/UP -
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<https://github.com/usgs-coupled/iphreeqc> -
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<https://git.gfz-potsdam.de/naaice/iphreeqc>
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- **tug** - <https://git.gfz-potsdam.de/naaice/tug>
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## Installation
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### Requirements
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To compile POET you need following software to be installed:
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- C/C++ compiler (tested with GCC)
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- MPI-Implementation (tested with OpenMPI and MVAPICH)
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- CMake 3.9+
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- Eigen3 3.4+ (required by `tug`)
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- *optional*: `doxygen` with `dot` bindings for documentation
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- R language and environment including headers or `-dev` packages
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(distro dependent)
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The following R packages (and their dependencies) must also be
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installed:
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- [Rcpp](https://cran.r-project.org/web/packages/Rcpp/index.html)
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- [RInside](https://cran.r-project.org/web/packages/RInside/index.html)
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- [qs](https://cran.r-project.org/web/packages/qs/index.html)
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This can be simply achieved by issuing the following commands:
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```sh
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# start R environment
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$ R
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# install R dependencies (case sensitive!)
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> install.packages(c("Rcpp", "RInside","qs"))
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> q(save="no")
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```
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### Clone the repository
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POET can be anonimously cloned from this repo over https. Make sure to
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also download the submodules:
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```sh
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git clone --recurse-submodules https://git.gfz-potsdam.de/naaice/poet.git
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```
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The `--recurse-submodules` option is a shorthand for:
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```sh
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cd poet
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git submodule init && git submodule update
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```
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### Compiling source code
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POET is built with CMake. You can generate Makefiles by running the
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usual:
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```sh
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mkdir build && cd build
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cmake ..
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```
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This will create the directory `build` and processes the CMake files
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and generate Makefiles from it. You're now able to run `make` to start
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build process.
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If everything went well you'll find the executables at
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`build/src/poet`, but it is recommended to install the POET project
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structure to a desired `CMAKE_INSTALL_PREFIX` with `make install`.
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During the generation of Makefiles, various options can be specified
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via `cmake -D <option>=<value> [...]`. Currently, there are the
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following available options:
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- **POET_DHT_Debug**=_boolean_ - toggles the output of detailed
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statistics about DHT usage. Defaults to _OFF_.
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- **POET_ENABLE_TESTING**=_boolean_ - enables small set of unit tests
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(more to come). Defaults to _OFF_.
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- **POET_PHT_ADDITIONAL_INFO**=_boolean_ - enabling the count of
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accesses to one PHT bucket. Use with caution, as things will get
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slowed down significantly. Defaults to _OFF_.
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- **POET_PREPROCESS_BENCHS**=*boolean* - enables the preprocessing of
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predefined models/benchmarks. Defaults to *ON*.
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- **USE_AI_SURROGATE**=*boolean* - includes the functions of the AI
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surrogate model. When active, CMake relies on `find_package()` to find
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an a implementation of `Threads` and a Python environment where Numpy
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and Keras need to be installed. Defaults to _OFF_.
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### Example: Build from scratch
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Assuming that only the C/C++ compiler, MPI libraries, R runtime
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environment and CMake have been installed, POET can be installed as
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follows:
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```sh
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# start R environment
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$ R
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# install R dependencies
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> install.packages(c("Rcpp", "RInside","qs"))
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> q(save="no")
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# cd into POET project root
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$ cd <POET_dir>
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# Build process
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$ mkdir build && cd build
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$ cmake -DCMAKE_INSTALL_PREFIX=/home/<user>/poet ..
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$ make -j<max_numprocs>
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$ make install
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```
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This will install a POET project structure into `/home/<user>/poet`
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which is called hereinafter `<POET_INSTALL_DIR>`. With this version of
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POET we **do not recommend** to install to hierarchies like
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`/usr/local/` etc.
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The correspondending directory tree would look like this:
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```sh
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poet
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├── bin
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│ ├── poet
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│ └── poet_init
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└── share
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└── poet
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├── barite
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│ ├── barite_200.rds
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│ ├── barite_200_rt.R
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│ ├── barite_het.rds
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│ └── barite_het_rt.R
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├── dolo
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│ ├── dolo_inner_large.rds
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│ ├── dolo_inner_large_rt.R
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│ ├── dolo_interp.rds
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│ └── dolo_interp_rt.R
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└── surfex
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├── PoetEGU_surfex_500.rds
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└── PoetEGU_surfex_500_rt.R
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```
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With the installation of POET, two executables are provided:
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- `poet` - the main executable to run simulations
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- `poet_init` - a preprocessor to generate input files for POET from
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R scripts
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Preprocessed benchmarks can be found in the `share/poet` directory
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with an according *runtime* setup. More on those files and how to
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create them later.
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## Running
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Run POET by `mpirun ./poet [OPTIONS] <RUNFILE> <SIMFILE>
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<OUTPUT_DIRECTORY>` where:
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- **OPTIONS** - POET options (explained below)
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- **RUNFILE** - Runtime parameters described as R script
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- **SIMFILE** - Simulation input prepared by `poet_init`
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- **OUTPUT_DIRECTORY** - path, where all output of POET should be
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stored
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### POET command line arguments
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The following parameters can be set:
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| Option | Value | Description |
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|-----------------------------|--------------|----------------------------------------------------------------------------------|
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| **--work-package-size=** | _1..n_ | size of work packages (defaults to _5_) |
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| **-P, --progress** | | show progress bar |
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| **--ai-surrogate** | | activates the AI surrogate chemistry model (defaults to _OFF_) |
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| **--dht** | | enabling DHT usage (defaults to _OFF_) |
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| **--qs** | | store results using qs::qsave() (.qs extension) instead of default RDS (.rds) |
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| **--dht-strategy=** | _0-1_ | change DHT strategy. **NOT IMPLEMENTED YET** (Defaults to _0_) |
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| **--dht-size=** | _1-n_ | size of DHT per process involved in megabyte (defaults to _1000 MByte_) |
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| **--dht-snaps=** | _0-2_ | disable or enable storage of DHT snapshots |
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| **--dht-file=** | `<SNAPSHOT>` | initializes DHT with the given snapshot file |
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| **--interp-size** | _1-n_ | size of PHT (interpolation) per process in megabyte |
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| **--interp-bucket-entries** | _1-n_ | number of entries to store at maximum in one PHT bucket |
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| **--interp-min** | _1-n_ | number of entries in PHT bucket needed to start interpolation |
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#### Additions to `dht-snaps`
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Following values can be set:
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- _0_ = snapshots are disabled
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- _1_ = only stores snapshot at the end of the simulation with name
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`<OUTPUT_DIRECTORY>.dht`
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- _2_ = stores snapshot at the end and after each iteration iteration
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snapshot files are stored in `<DIRECTORY>/iter<n>.dht`
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### Example: Running from scratch
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We will continue the above example and start a simulation with
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*barite_het*, which simulation files can be found in
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`<INSTALL_DIR>/share/poet/barite/barite_het*`. As transport a
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heterogeneous diffusion is used. It's a small 2D grid, 2x5 grid,
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simulating 50 time steps with a time step size of 100 seconds. To
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start the simulation with 4 processes `cd` into your previously
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installed POET-dir `<POET_INSTALL_DIR>/bin` and run:
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```sh
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cp ../share/poet/barite/barite_het* .
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mpirun -n 4 ./poet barite_het_rt.R barite_het.rds output
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```
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After a finished simulation all data generated by POET will be found
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in the directory `output`.
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You might want to use the DHT to cache previously simulated data and
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reuse them in further time-steps. Just append `--dht` to the options
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of POET to activate the usage of the DHT. Also, after each iteration a
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DHT snapshot shall be produced. This is done by appending the
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`--dht-snaps=<value>` option. The resulting call would look like this:
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```sh
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mpirun -n 4 ./poet --dht --dht-snaps=2 barite_het_rt.R barite_het.rds output
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```
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### Example: Preparing Environment and Running with AI surrogate
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To run the AI surrogate, you need to have a Keras installed in your
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Python environment. The implementation in POET is agnostic to the exact
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Keras version, but the provided model file must match your Keras version.
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Using Keras 3 with `.keras` model files is recommended. The compilation
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process of POET remains mostly the same as shown above, but the CMake
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option `-DUSE_AI_SURROGATE=ON` must be set.
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To use the AI surrogate, you must declare several values in the R input
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script. This can be either done directly in the input script or in an
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additional file. This file can be provided by adding the file path as the
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element `ai_surrogate_input_script` to the `chemistry_setup` list in the
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R input script.
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The following variables and functions must be declared:
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- `model_file_path` [*string*]: Path to the Keras model file with which
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the AI surrogate model is initialized.
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- `validate_predictions(predictors, prediction)` [*function*]: Returns a boolean
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vector of length `nrow(predictions)`. The output of this function defines
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which predictions are considered valid and which are rejected. Regular
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simulation will only be done for the rejected values, and the results
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will be added to the training data buffer of the AI surrogate model.
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Can eg. be implemented as a mass balance threshold between the predictors
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and the prediction.
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The following variables and functions can be declared:
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- `batch_size` [*int*]: Batch size for the inference and training functions,
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defaults to 2560.
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- `training_epochs` [*int*]: Number of training epochs with each training data
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set, defaults to 20.
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- `training_data_size` [*int*]: Size of the training data buffer. After the
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buffer has been filled, the model starts training and removes this amount of
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data from the front of the buffer. Defaults to the size of the Field.
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- `use_Keras_predictions` [*bool*]: Decides if the Keras prediction function
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should be used instead of the custom C++ implementation (Keras might be faster
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for larger models, especially on GPU). Defaults to false.
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- `disable_training` [*bool*]: Deactivates the training functions.
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- `save_model_path` [*string*]: After each training step the current model
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is saved to this path as a .keras file.
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- `preprocess(df)` [*function*]:
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Returns the scaled/transformed data frame. The default implementation uses no
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scaling or transformations.
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- `postprocess(df)` [*function*]:
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Returns the rescaled/backtransformed data frame. The combination of preprocess() and postprocess() is expected to be idempotent. The default implementation uses no
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scaling or transformations.
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```sh
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cd <installation_dir>/bin
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# copy the benchmark files to the installation directory
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cp <project_root_dir>/bench/barite/{barite_50ai*,db_barite.dat,barite.pqi} .
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# preprocess the benchmark
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./poet_init barite_50ai.R
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# run POET with AI surrogate and GPU utilization
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srun --gres=gpu -N 1 -n 12 ./poet --ai-surrogate barite_50ai_rt.R barite_50ai.rds output
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```
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Keep in mind that the AI surrogate is currently not stable or might also not
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produce any valid predictions.
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## Defining a model
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In order to provide a model to POET, you need to setup a R script
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which can then be used by `poet_init` to generate the simulation
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input. Which parameters are required can be found in the
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[Wiki](https://git.gfz-potsdam.de/naaice/poet/-/wikis/Initialization).
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We try to keep the document up-to-date. However, if you encounter
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missing information or need help, please get in touch with us via the
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issue tracker or E-Mail.
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`poet_init` can be used as follows:
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```sh
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./poet_init [-o, --output output_file] [-s, --setwd] <script.R>
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```
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where:
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- **output** - name of the output file (defaults to the input file
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name with the extension `.rds`)
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- **setwd** - set the working directory to the directory of the input
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file (e.g. to allow relative paths in the input script). However,
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the output file will be stored in the directory from which
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`poet_init` was called.
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## About the usage of MPI_Wtime()
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Implemented time measurement functions uses `MPI_Wtime()`. Some
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important information from the OpenMPI Man Page:
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For example, on platforms that support it, the clock_gettime()
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function will be used to obtain a monotonic clock value with whatever
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precision is supported on that platform (e.g., nanoseconds). |