# suanPan
**Repository Path**: jixiangshui/suanPan
## Basic Information
- **Project Name**: suanPan
- **Description**: 🧮 An Open Source, Parallel and Heterogeneous Finite Element Analysis Framework
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: dev
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-19
- **Last Updated**: 2022-01-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# 
suanPan
[](https://doi.org/10.5281/zenodo.1285221)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://suanpan-manual.readthedocs.io/?badge=latest)
[](https://github.com/TLCFEM/suanPan/releases)
[](https://snapcraft.io/suanpan)
[](https://chocolatey.org/packages/suanpan)
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## Introduction
[🧮 **suanPan**](https://tlcfem.github.io/suanPan/) is a finite element method (FEM) simulation platform for applications in fields such as solid mechanics and civil/structural/seismic engineering. The name **suanPan** (in some places such as suffix it is also abbreviated as **suPan**) comes from the term *Suan Pan* (算盤), which is [Chinese abacus](https://en.wikipedia.org/wiki/Suanpan). **suanPan** is written in modern high quality C++ code and is targeted to provide an efficient, concise, flexible and reliable FEM simulation platform.
**suanPan** is partially influenced by popular (non-)commercial FEA packages, such as [ABAQUS UNIFIED FEA](https://www.3ds.com/products-services/simulia/products/abaqus/), [ANSYS](http://www.ansys.com/) and [OpenSees](http://opensees.berkeley.edu/).
Please check documentation [here](https://tlcfem.gitbook.io/suanpan-manual/) and [here](http://suanpan-manual.rtfd.io/) for command references. Please consider star ⭐ the project!
## Features
The highlights of **suanPan** are
- **suanPan** is *fast*, both memory and thread safe.
- **suanPan** is designed based on the [shared memory](https://en.wikipedia.org/wiki/Shared_memory) model and supports parallelism on heterogeneous architectures, for example multi-threaded CPU + optional GPU. The parallelism is available for both element state updating and global matrix assembling.
- **suanPan** is open source and easy to be expanded to incorporate user-defined elements, materials, etc.
- **suanPan** separates the FEA model part from the linear algebra operation part, which significantly reduces the complexity of development.
- **suanPan** utilizes the new language features shipped with the latest standards (C++14, C++17, etc.), such as new STL containers, smart pointers and many others.
- **suanPan** supports simple visualization supported by [VTK](https://vtk.org/).
## Quick Start
Sample models are available for almost all models/commands. Please check the `Example` folder for details.
## Installation
Only 64-bit version is compiled. It is assumed that [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) is available thus if the program fails, please check if your CPU supports AVX.
### Windows
#### Portable Binary
The archive of binaries are released under [Release](https://github.com/TLCFEM/suanPan/releases) page.
1. `suanpan-win-mkl-vtk.zip` is the portable version.
2. `suanpan-win-mkl-vtk.exe` is the installer.
Simply download, unpack/install and run. A batch file named as `AddAssociation.bat` is provided in the archive. It provides file associations and prepares a proper working environment (build system, autocompletion, highlighting) with [Sublime Text](https://www.sublimetext.com/). Further details can be seen [here](https://suanpan-manual.readthedocs.io/Tutorial/Obtain/).
#### Chocolatey
The binaries, which are compiled with Intel MKL and VTK, are available on [Chocolatey](https://chocolatey.org/packages/suanpan), please use the following command to install the package.
1. Follow the [instructions](https://chocolatey.org/install) to install Chocolatey.
2. Use the following command to install `suanPan`.
```
choco install suanpan
```
3. It is recommended to use a modern terminal such as [Windows Terminal](https://github.com/microsoft/terminal) and [Fluent Terminal](https://github.com/felixse/FluentTerminal) for better output display.
[](https://asciinema.org/a/418539)
#### Scoop
It is also possible to use [Scoop](https://scoop.sh/) to install the package.
1. Install [Scoop](https://scoop.sh/).
```ps
Set-ExecutionPolicy RemoteSigned -scope CurrentUser
iwr -useb get.scoop.sh | iex
```
2. Install suanPan.
```ps
scoop install suanpan
```
### Linux
Linux users are recommended to obtain the binaries via snap. The snap supports visualization via VTK and uses Intel MKL for linear algebra.
[](https://snapcraft.io/suanpan)
[](https://asciinema.org/a/418407)
### Other Platforms
Precompiled binaries are provided via CI/CD on MacOS, Windows and Ubuntu. Please download the file from the [release](https://github.com/TLCFEM/suanPan/releases) page.
Advanced users can compile the program from source by themselves in order to enable
1. GPU based solvers which require available [CUDA](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/) library;
2. Visualization support provided by VTK library;
3. High performing linear algebra provided by Intel MKL library.
On Windows, to add file associations with `.sp` and `.supan` files, please run the `AddAssociation.bat` file with admin privilege. [Sublime Text](https://www.sublimetext.com/) autocompletion and syntax highlighting files are also provided. Please check the `Enhancement` folder.
On Linux, since CI/CD uses `GCC 9.3.0`, it may be required to update/install GCC version 9 or above.
```bash
sudo apt install gcc-9 g++-9 gfortran-9 libomp5
```
For VTK enabled versions, it may be necessary to install OpenGL.
```bash
sudo apt install libglu1-mesa-dev freeglut3-dev mesa-common-dev
```
## Dependency
Additional libraries used in **suanPan** are listed as follows.
- [**ARPACK**](https://www.caam.rice.edu/software/ARPACK/) version 0.96
- [**SPIKE**](http://www.spike-solver.org/) version 1.0
- [**FEAST**](http://www.feast-solver.org/) version 4.0
- [**SuperLU**](https://portal.nersc.gov/project/sparse/superlu/) version 5.3.0 and [**SuperLU MT**](https://portal.nersc.gov/project/sparse/superlu/) version 3.1
- [**OpenBLAS**](https://github.com/xianyi/OpenBLAS) version 0.3.15
- [**TBB** Threading Building Blocks](https://github.com/oneapi-src/oneTBB) version 2021.4.0
- [**HDF5**](https://www.hdfgroup.org/solutions/hdf5/) version 1.10.6
- [**MUMPS**](http://mumps.enseeiht.fr/) version 5.2.1
- [**VTK**](https://vtk.org/) version 8.2
- [**CUDA**](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/) version 11.5
- [**Armadillo**](http://arma.sourceforge.net/) version 10.7
- [**Intel oneAPI Math Kernel Library**](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onemkl.html) version 2021.2.0
Those libraries may depend on other libraries such as [zlib](https://zlib.net/) and [Szip](https://support.hdfgroup.org/doc_resource/SZIP/). Additional tools may be used by **suanPan**, they are
- [**UPX** the Ultimate Packer for eXecutables](https://upx.github.io/)
## How To Compile
Please refer to the corresponding [page](https://github.com/TLCFEM/suanPan-manual/blob/dev/docs/Tutorial/Compile.md) in manual for details.
## Happy Modelling
If you find `suanPan` useful, you can buy me a bag of tea via this [link](https://www.paypal.com/donate/?hosted_button_id=29RHR5SN9CWMG).
## License
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