Merge branch 'PhasicFlow:main' into main
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Omid Khosravi
GitHub
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# How to build PhasicFlow {#howToBuildPhasicFlow}
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You can build PhasicFlow for CPU or GPU. You can have a single build or oven multiple builds on a machine. Here you learn how to have a single build of PhasicFlow, in various modes of execution.
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# Required packages
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You need a list of packaged installed on your computer before building PhasicFlow:
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* git, for cloning the code and package management
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* g++, for compiling the code
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* cmake, for generating build system
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* tbb, a parallel library for STL algorithms
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* Cuda (if GPU is targeted), for compiling the code for CUDA execution.
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* Kokkos, the parallelization backend of PhasicFlow
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### git
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if git is not installed on your computer, enter the following commands
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```
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$ sudo apt update
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$ sudo apt install git
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```
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### g++ (C++ compiler)
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The code is tested with g++ (gnu C++ compiler). The default version of g++ on Ubuntu 18.04 LTS or upper is sufficient for compiling. If it is not installed on your operating system, enter the following command:
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```
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$ sudo apt update
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$ sudo apt install g++
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```
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### CMake
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You also need to have CMake-3.22 or higher installed on your computer.
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```
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$ sudo apt update
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$ sudo apt install cmake
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```
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### tbb (2020.1-2 or higher)
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For **Ubuntu 20.04 LTS or higher versions**, you can install tbb using apt. For now, some parallel algorithms on host side rely on tbb parallel library (C++ parallel backend). Use e following commands to install it:
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```
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$ sudo apt update
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$ sudo apt install libtbb-dev
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```
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If you are compiling on **Ubuntu-18.04 LTS**, you need to enter the following commands to get the right version (2020.1-2 or higher) of tbb:
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```
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$ wget "http://archive.ubuntu.com/ubuntu/pool/universe/t/tbb/libtbb2_2020.1-2_amd64.deb"
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$ sudo dpkg --install libtbb2_2020.1-2_amd64.deb
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$ wget "http://archive.ubuntu.com/ubuntu/pool/universe/t/tbb/libtbb-dev_2020.1-2_amd64.deb"
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$ sudo dpkg --install libtbb-dev_2020.1-2_amd64.deb
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```
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### Cuda
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If you want to build PhasicFlow to be executed on an nvidia-GPU, you need to install the latest version of Cuda compiler, which is compatible with your hardware and OS, on your computer.
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# How to build?
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Here you will learn how to build PhasicFlow for single execution mode. Follow the steps below to install it on your computer.
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Tested operating systems are:
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* Ubuntu 18.04 LTS
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* Ubuntu 20.04 LTS
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* Ubuntu 22.04 LTS
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### Step 1: Package check
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Make sure that you have installed all the required packages on your computer. See above for more information.
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### Step 2: Cloning Kokkos
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It is assumed that Kokkos source is located in the home folder of your computer. Clone the latest version of Kokkos into your home folder:
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```
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$ cd ~
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$ mkdir Kokkos
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$ cd Kokkos
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$ git clone https://github.com/kokkos/kokkos.git
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```
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or simply download and extract the source code of Kokkos in `~/Kokkos` folder. In the end, the top level CMakeLists.txt file should be located in `~/Kokkos/kokkos` folder.
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### Step 3: Cloning PhasicFlow
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Create the PhasicFlow folder in your home folder and then clone the source code into that folder:
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```
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$ cd ~
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$ mkdir PhasicFlow
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$ cd PhasicFlow
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$ git clone https://github.com/PhasicFlow/phasicFlow.git
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```
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### Step 4: Environmental variables
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Opne the bashrc file using the following command:
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`$ gedit ~/.bashrc`
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and add the following line to the end of the file, **save** and **close** it.
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`source $HOME/PhasicFlow/phasicFlow/cmake/bashrc`
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this will introduce a new source file for setting the environmental variables of PhasicFlow. If you want to load these variables in the current open terminal, you need to source it. Or, simply **close the terminal** and **open a new terminal**.
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### Step 5: Building PhasicFlow
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Follow one of the followings to build PhasicFlow for one mode of execution.
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#### Serial build for CPU
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In a **new terminal** enter the following commands:
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```
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$ cd ~/PhasicFlow/phasicFlow
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$ mkdir build
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$ cd build
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$ cmake ../ -DpFlow_Build_Serial=On
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$ make install
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```
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For faster builds, use `make install -j`. This will use all the CPU cores on your computer for building.
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#### OpenMP build for CPU
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```
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$ cd ~/PhasicFlow/phasicFlow
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$ mkdir build
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$ cd build
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$ cmake ../ -DpFlow_Build_OpenMP=On
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$ make install
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```
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#### GPU build for parallel execution on CUDA-enabled GPUs
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```
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$ cd ~/PhasicFlow/phasicFlow
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$ mkdir build
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$ cd build
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$ cmake ../ -DpFlow_Build_Cuda=On
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$ make install
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```
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After building, `bin`, `include`, and `lib` folders will be created in `~/PhasicFlow/phasicFlow/` folder. Now you are ready to use PhasicFlow.
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**note 1**: When compiling the code in parallel, you need to have enough RAM on your computer. As a rule, you need 1 GB free RAM per each processor in your computer for compiling in parallel.
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You may want to use fewer number of cores on your computer by using the following command:
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`$ make install -j 3`
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the above command uses only 3 cores for compiling.
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**note 2**: By default PhasicFlow is compiled with **double** as floating point variable. You can compile it with **float**. Just in the command line of camke added `-DpFlow_Build_Double=Off` flag to compile it with float. For example if you are building for cuda, you can enter the following command:
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`$ cmake ../ -DpFlow_Build_Cuda=On --DpFlow_Build_Double=Off`
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### Step 6: Testing
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In the current terminal or a new terminal enter the following command:
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`$ ~checkPhasicFlow`
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This command shows the host and device environments and software version. If PhasicFlow was build correctly, you would get the following output:
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```
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Initializing host/device execution spaces . . .
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Host execution space is Serial
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Device execution space is Cuda
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ou are using phasicFlow v-0.1 (copyright(C): www.cemf.ir)
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In this build, double is used for floating point operations.
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Finalizing host/device execution space ....
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```
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PhasicFlow is an open-source, parallel, multi-artitecture code for discrete element simulation (DEM) simulation of granular and multi-phase flows. It is developed in C++ and can be built and executed on **multi-core CPUs** or **GPUs**. It is fast and scalable code for large simulation. For now, we have performed simulations upto 32,000,000 particles on mid-range GPUs. The calculations can be done based on single precision (float type in C++) or double precision (double type in C++). This option can be selected when the code is first built.
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It solves the Newton's second law of motion and Euler's second law of motion for the translational and rotational motion of spherical particles. The code is not restricted to a specifict geometry or wall motion. It is designed in a way that additional features can be added to the code very easily.
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PhasicFlow is an open-source, parallel, multi-architecture code for discrete element simulation (DEM) simulation of granular and multi-phase flows. It is developed in C++ and can be built and executed on **multi-core CPUs** or **GPUs**. It is fast and scalable code for large simulation. For now, we have performed simulations up-to 32,000,000 particles on mid-range GPUs. The calculations can be done based on single precision (float type in C++) or double precision (double type in C++). This option can be selected when the code is first built.
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It solves the Newton's second law of motion and Euler's second law of motion for the translational and rotational motion of spherical particles. The code is not restricted to a specific geometry or wall motion. It is designed in a way that additional features can be added to the code very easily.
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## Explore more
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* [Features of PhasicFlow](@ref phasicFlowFeatures)
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* [How to build PhasicFlow](https://github.com/PhasicFlow/phasicFlow/wiki/How-to-Build-PhasicFlow)
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* [How to build PhasicFlow](@ref howToBuildPhasicFlow)
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* How to setup and run granular flow simulations
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* [Benchmarks and performance tests](https://github.com/PhasicFlow/phasicFlow/wiki/Performance-of-phasicFlow)
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## Building options
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You can build PhasicFlow to be executed on multi-core CPUs or GPUs. It is also possible to select the type of floating point variables in PhasicFlow: double or float. float type requires less memory and mostly consumes less time of a processor to complete a mathematical operation. So, there is a benefit for using floats in DEM simulation specially when GPU is targeted for computations.
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Build options for PhasicFlow:
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* **serial (double or sinlge type)**: execution on one cpu core
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* **OpenMp (double or single type)**: execution on multiple cores of a CPU
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* **cuda (double or sngle type)**: execution on cuda-enabled GPUs
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* **serial (double or float type)**: execution on one cpu core
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* **OpenMp (double or float type)**: execution on multiple cores of a CPU
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* **cuda (double or float type)**: execution on cuda-enabled GPUs
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