89 lines
4.7 KiB
Markdown
89 lines
4.7 KiB
Markdown
# MD-Bench
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MD-Bench is a toolbox for the performance engineering of short-range force calculation kernels on molecular-dynamics applications.
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It aims at covering all available state-of-the-art algorithms from different community codes such as LAMMPS and GROMACS.
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Apart from that, many tools to study and evaluate the in-depth performance of such kernels on distinct hardware are offered, like the gather-bench which is a benchmark to mimic the data movement from such kernels and the stubbed force calculation cases used to isolate the impacts caused by memory latencies and control flow divergence.
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| Verlet Lists | GROMACS MxN | Stubbed cases |
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| ---------------------------------------------- | -------------------------------------------------- | ------------------------------------------------- |
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## Build instructions
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Properly configure your building by changing `config.mk` file. The following options are available:
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- **TAG:** Compiler tag (available options: GCC, CLANG, ICC, ONEAPI, NVCC).
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- **ISA:** Instruction set (available options: SSE, AVX, AVX2, AVX512).
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- **MASK\_REGISTERS:** Use AVX512 mask registers (always true when ISA is set to AVX512).
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- **OPT\_SCHEME:** Optimization algorithm (available options: lammps, gromacs).
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- **ENABLE\_LIKWID:** Enable likwid to make use of HPM counters.
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- **DATA\_TYPE:** Floating-point precision (available options: SP, DP).
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- **DATA\_LAYOUT:** Data layout for atom vector properties (available options: AOS, SOA).
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- **ASM\_SYNTAX:** Assembly syntax to use when generating assembly files (available options: ATT, INTEL).
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- **DEBUG:** Toggle debug mode.
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- **EXPLICIT\_TYPES:** Explicitly store and load atom types.
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- **MEM\_TRACER:** Trace memory addresses for cache simulator.
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- **INDEX\_TRACER:** Trace indexes and distances for gather-md.
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- **COMPUTE\_STATS:** Compute statistics.
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Configurations for LAMMPS Verlet Lists optimization scheme:
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- **ENABLE\_OMP\_SIMD:** Use omp simd pragma on half neighbor-lists kernels.
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- **USE\_SIMD\_KERNEL:** Compile kernel with explicit SIMD intrinsics.
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Configurations for GROMACS MxN optimization scheme:
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- **USE\_REFERENCE\_VERSION:** Use reference version (only for correction purposes).
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- **XTC\_OUTPUT:** Enable XTC output.
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- **HALF\_NEIGHBOR\_LISTS\_CHECK\_CJ:** Check if j-clusters are local when decreasing the reaction force.
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Configurations for CUDA:
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- **USE\_CUDA\_HOST\_MEMORY:** Use CUDA host memory to optimize host-device transfers.
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When done, just use `make` to compile the code.
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You can clean intermediate build results with `make clean`, and all build results with `make distclean`.
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You have to call `make clean` before `make` if you changed the build settings.
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## Usage
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Use the following command to run a simulation:
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```bash
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./MD-Bench-<TAG>-<OPT_SCHEME> [OPTION]...
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```
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Where `TAG` and `OPT_SCHEME` correspond to the building options with the same name.
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Without any options, a Copper FCC lattice system with size 32x32x32 (131072 atoms) over 200 time-steps using the Lennard-Jones potential (sigma=1.0, epsilon=1.0) is simulated.
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The default behavior and other options can be changed using the following parameters:
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```
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-p <string>: file to read parameters from (can be specified more than once)
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-f <string>: force field (lj or eam), default lj
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-i <string>: input file with atom positions (dump)
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-e <string>: input file for EAM
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-n / --nsteps <int>: set number of timesteps for simulation
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-nx/-ny/-nz <int>: set linear dimension of systembox in x/y/z direction
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-r / --radius <real>: set cutoff radius
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-s / --skin <real>: set skin (verlet buffer)
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--freq <real>: processor frequency (GHz)
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--vtk <string>: VTK file for visualization
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--xtc <string>: XTC file for visualization
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```
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## Examples
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## Citations
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R. Ravedutti Lucio Machado, J. Eitzinger, H. Köstler, and G. Wellein: MD-Bench: A generic proxy-app toolbox for state-of-the-art molecular dynamics algorithms. Accepted for [PPAM](https://ppam.edu.pl/) 2022, the 14th International Conference on Parallel Processing and Applied Mathematics, Gdansk, Poland, September 11-14, 2022. PPAM 2022 Best Paper Award. Preprint: [arXiv:2207.13094](https://arxiv.org/abs/2207.13094)
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## Credits
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MD-Bench is developed by the Erlangen National High Performance Computing Center ([NHR@FAU](https://hpc.fau.de/)) at the University of Erlangen-Nürnberg.
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## License
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[LGPL-3.0](https://github.com/RRZE-HPC/MD-Bench/blob/master/LICENSE)
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