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**PhasicFlow** is a parallel C++ code for performing DEM simulations. It can run on shared-memory multi-core computational units such as multi-core CPUs or GPUs (for now it works on CUDA-enabled GPUs). The parallelization method mainly relies on loop-level parallelization on a shared-memory computational unit. You can build and run PhasicFlow in serial mode on regular PCs, in parallel mode for multi-core CPUs, or build it for a GPU device to off-load computations to a GPU. In its current statues you can simulate millions of particles (up to 80M particles tested) on a single desktop computer. You can see the [performance tests of PhasicFlow](https://github.com/PhasicFlow/phasicFlow/wiki/Performance-of-phasicFlow) in the wiki page.
**PhasicFlow** is a parallel C++ code for performing DEM simulations. It can run on shared-memory multi-core computational units such as multi-core CPUs or GPUs (for now it works on CUDA-enabled GPUs). The parallelization method mainly relies on loop-level parallelization on a shared-memory computational unit. You can build and run PhasicFlow in serial mode on regular PCs, in parallel mode for multi-core CPUs, or build it for a GPU device to off-load computations to a GPU. In its current statues you can simulate millions of particles (up to 80M particles tested) on a single desktop computer. You can see the [performance tests of PhasicFlow](https://github.com/PhasicFlow/phasicFlow/wiki/Performance-of-phasicFlow) in the wiki page.
**MPI** parallelization with dynamic load balancing is under development. With this level of parallelization, PhasicFlow can leverage the computational power of **multi-gpu** workstations or clusters with distributed memory CPUs.
In summary PhasicFlow can have 6 execution modes:
1. Serial on a single CPU,
2. Parallel on a multi-core computer/node (using OpenMP),
3. Parallel on an nvidia-GPU (using Cuda),
4. Parallel on distributed memory workstation (Using MPI)
5. Parallel on distributed memory workstations with multi-core nodes (using MPI+OpenMP)
6. Parallel on workstations with multiple GPUs (using MPI+Cuda).
## How to build?
You can build PhasicFlow for CPU and GPU executions. [Here is a complete step-by-step procedure](https://github.com/PhasicFlow/phasicFlow/wiki/How-to-Build-PhasicFlow).