XFluids

XFluids: A unified cross-architecture heterogeneous reacting flows simulation solver

XFluids is a parallelized SYstem-wide Compute Language (SYCL) C++ solver for large-scale high-resolution simulations of compressible multi-component reacting flows. It is developed by Prof. Shucheng Pan's group at the School of Aeronautics, Northwestern Polytechincal University.

main developers:

Jinlong Li (ljl66623@mail.nwpu.edu.cn)
Shucheng Pan (shucheng.pan@nwpu.edu.cn)

other contributors:

Yixuan Lian, Renfei Zhang

References

If you use XFluids for academic aplications, please cite our paper:

Jinlong Li, Shucheng Pan (2024). XFluids: A unified cross-architecture heterogeneous reacting flows simulation solver and its applications for multi-component shock-bubble interactions. arXiv:2403.05910. (https://arxiv.org/abs/2403.05910)

Features

Support CPU, GPU (integrated & discrete), and FPGA without porting the code
General for multi-vendor devices (Intel/NVIDIA/AMD/Hygon ... )
High portability, productivity, and performace
GPU-aware MPI
Highly optimized kernels & device functions for multicomponent flows and chemical reaction
ongoing work: sharp-interface method, curvilinear mesh, turbulence models ...

Supported GPUs

The following gpus have been tested:

NVIDIA
- Data center GPU: A100, P100
- Gaming GPS: RTX 4090, RTX 3090/3080/3070/3060TI, T600, RTX 1080
AMD
- Data center GPU: MI50
- Gaming GPS: RX 7900XTX, RX 6800XT, Pro VII
Intel
- Gaming GPS: ARC A770/A380
- Integrated GPUs: UHD P630, UHD 750

1. Manually installed Dependencies

1.1. Conda or Conda mirror soure for Chinese users basic environment, at least version 23.9.0

1.2. One of two LLVM implementation:

NOTE: SYCL implementation of AdaptiveCpp is strongly recommended for XFluids, and the support of Intel oneAPI will be deprecated.

LLVM version >= 14.0 for SYCL implementation of AdaptiveCpp(known as OpenSYCL/hipSYCL)

Install llvm from org.

sudo bash -c "$(wget -O - https://apt.llvm.org/llvm.sh)" # instal llvm(version 18 june June/2024) automatically
sudo apt install libclang-18-dev libomp-18-dev -y        # additional packages of llvm(libclang-18-dev, libomp-18-dev for llvm-18)

An internal AdaptiveCpp is compiled by XFluids by default, but if use a system installed AdaptiveCpp, please set cmake option "AdaptiveCpp_DIR".

cmake -DAdaptiveCpp_DIR=/path/to/AdaptiveCpp/lib/cmake/AdaptiveCpp ..

Intel oneAPI version >= 2023.0.0 for SYCL implementation of Intel, and codeplay Solutions if NVIDIA and AMD targeting backends are needed
- activate environment for oneAPI appended codeplay sultion libs
```
source ./scripts/oneAPI/oneapi_base.sh
```

1.3. Linux system packages: cmake, scons

2. Select target device in SYCL project

2.1. Device discovery

2.1.1 AdaptiveCpp device discovery: exec "acpp-info" in cmd for device counting

$ acpp-info
=================Backend information===================
Loaded backend 0(platform_id): OpenMP
  Found device: hipSYCL OpenMP host device
Loaded backend 1(platform_id): CUDA
  Found device: NVIDIA GeForce RTX 3070
  Found device: NVIDIA GeForce RTX 3070
=================Device information===================
***************** Devices for backend OpenMP *****************
Device 0(device_id)
***************** Devices for backend CUDA *****************
Device 0(device_id)
***************** Devices for backend CUDA *****************
Device 1(device_id)

2.1.2. Intel oneAPI device discovery: exec "sycl-ls" in cmd for device counting

$ sycl-ls
[opencl:acc(platform_id:0):0(device_id)] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device 1.2
[opencl:cpu(platform_id:1):1(device_id)] Intel(R) OpenCL, AMD Ryzen 7 5800X 8-Core Processor 3.0
[ext_oneapi_cuda:gpu(platform_id:2):0(device_id)] NVIDIA CUDA BACKEND, NVIDIA T600 0.0 [CUDA 11.5]
[ext_oneapi_cuda:gpu(platform_id:2):1(device_id)] NVIDIA CUDA BACKEND, NVIDIA T600 0.0 [CUDA 11.5]

2.2. Queue construction: set integer platform_id and device_id("DeviceSelect" in json file or option: -dev)

NOTE: platform_id and device_id are revealed in [2.1-Device-discovery]("2.1. Device discovery")

auto device = sycl::platform::get_platforms()[platform_id].get_devices()[device_id];
sycl::queue q(device);

3. Compile and usage of this project

3.1. Read root <XFluids/CMakeLists.txt>

CMAKE_BUILD_TYPE is set to "Release" by default, SYCL code would target to host while ${CMAKE_BUILD_TYPE}==Debug
set INIT_SAMPLE as the problem being tested, path to "species_list.dat" and "reaction_list.dat" should be given to MIXTURE_MODEL
MPI and AWARE-MPI support added in project, AWARE_MPI need specific GPU-ENABLED mpi version, details referenced in [4-mpi-libs]("4. MPI libs")
VENDOR_SUBMIT allows throwing some parallism tuning cuda/hip model to their GPU, only supportted by AdaptiveCpp compile environment

3.2. BUILD and RUN

build with cmake

cd ./XFluids
mkdir build && cd ./build && cmake .. && make -j

3.2.1. Local machine running
XFluids automatically read <XFluids/settings/*.json> file depending on INIT_SAMPLE setting
```
./XFluids
```
Append options to XFluids in cmd for another settings, all options are optional, all options are listed in [6. executable file options]("6. Executable file options")
```
./XFluids -dev=1,1,0
mpirun -n mx*my*mz ./XFluids -mpi=mx,my,mz -dev=1,0,0
```

3.2.2. Slurm sbatch running on Hygon(KunShan) supercompute center

cd ./XFluids/scripts/KS-DCU
sbatch ./1node.slurm
sbatch ./2node.slurm

4. MPI libs

NOTE: MPI functionality is not supported by Intel oneAPI SYCL implementation

4.1. Set MPI_PATH browsed by cmake before build

cmake system of this project browse libmpi.so automatically in path of ${MPI_PATH}/lib, please export MPI_PATH to the mpi you want:
```
export MPI_PATH=/home/ompi
```

4.2. The value of MPI_HOME, MPI_INC, path of MPI_CXX(libmpi.so/linmpicxx.so) output on screen while it is found

  -- MPI settings:
  --   MPI_HOME:/home/ompi
  --   MPI_INC: /home/ompi/include added
  --   MPI_CXX lib located: /home/ompi/lib/libmpi.so found

5. .json configure file arguments

reading commits in src file: <${workspaceFolder}/src/read_ini/settings/read_json.cpp>

6. Executable file options

name of options	function	type
-domain	domain size : length, width, height	float
-run	domain resolution and running steps: X_inner,Y_inner,Z_inner,nStepmax(if given)	int
-blk	initial local work-group size, dim_blk_x, dim_blk_y, dim_blk_z,DtBlockSize(if given)	int
-dev	device counting and selecting: device munber,platform,device	int
-mpi	mpi cartesian size: mx,my,mz	int
-mpi-s	"weak" or "strong"	std::string
-mpidbg	append the option with or without value to open mpi multi-rank debug	just append

7. Output data format

NOTE: Output data format is controlled by the value of "OutDAT", "OutVTI" in .json file

7.1. Tecplot file

import .dat files of all ranks of one Step for visualization, points overlapped between boundarys of ranks(3D parallel tecplot format file visualization is not supportted, using tecplot for 1D visualization is recommended)

7.2. VTK file

use paraview to open *.pvti files for MPI visualization(1D visualization is not allowed, using paraview for 2/3D visualization is recommended);

Cite XFluids

@misc{li2024xfluids,
      title={XFluids: A unified cross-architecture heterogeneous reacting flows simulation solver and its applications for multi-component shock-bubble interactions}, 
      author={Jinlong Li and Shucheng Pan},
      year={2024},
      eprint={2403.05910},
      archivePrefix={arXiv}
}

Acknowledgments

XFluids has received financial support from the following fundings:

The Guanghe foundation (Grant No. ghfund202302016412)
The National Natural Science Foundation of China (Grant No. 11902271)