PRACE Benchmarks for GPAW ========================= GPAW ---- ### Code description [GPAW](https://wiki.fysik.dtu.dk/gpaw/) is a density-functional theory (DFT) program for ab initio electronic structure calculations using the projector augmented wave method. It uses a uniform real-space grid representation of the electronic wavefunctions that allows for excellent computational scalability and systematic converge properties. GPAW is written mostly in Python, but includes also computational kernels written in C as well as leveraging external libraries such as NumPy, BLAS and ScaLAPACK. Parallelisation is based on message-passing using MPI with no support for multithreading. Development branches for GPGPUs and MICs include support for offloading to accelerators using either CUDA or pyMIC/libxsteam, respectively. ### Download GPAW is freely available under the GPL license. The source code can be downloaded from the [Git repository](https://gitlab.com/gpaw/gpaw) or as a tar package for each release from [PyPi](https://pypi.org/simple/gpaw/). For example, to get version 1.4.0 using git: ```bash git clone -b 1.4.0 https://gitlab.com/gpaw/gpaw.git ``` ### Install Generic [installation instructions](https://wiki.fysik.dtu.dk/gpaw/install.html) and [platform specific examples](https://wiki.fysik.dtu.dk/gpaw/platforms/platforms.html) are provided in the [GPAW wiki](https://wiki.fysik.dtu.dk/gpaw/). For accelerators, architecture specific instructions and requirements are also provided for [Xeon Phis](build/build-xeon-phi.md) and for [GPGPUs](build/build-cuda.md). Example [build scripts](build/examples/) are also available for some PRACE systems. Benchmarks ---------- ### Download The benchmark set is available in the [benchmark/](benchmark/) directory or alternatively, for download, either directly from the development [Git repository](https://github.com/mlouhivu/gpaw-benchmarks/tree/prace) or from the PRACE RI website (http://www.prace-ri.eu/ueabs/). To download the benchmarks, use e.g. the following command: ``` git clone -b prace https://github.com/mlouhivu/gpaw-benchmarks ``` ### Benchmark cases #### Case S: Carbon nanotube A ground state calculation for a carbon nanotube in vacuum. By default uses a 6-6-10 nanotube with 240 atoms (freely adjustable) and serial LAPACK with an option to use ScaLAPACK. Expected to scale up to 10 nodes and/or 100 MPI tasks. Input file: [benchmark/carbon-nanotube/input.py](benchmark/carbon-nanotube/input.py) #### Case M: Copper filament A ground state calculation for a copper filament in vacuum. By default uses a 2x2x3 FCC lattice with 71 atoms (freely adjustable) and ScaLAPACK for parallelisation. Expected to scale up to 100 nodes and/or 1000 MPI tasks. Input file: [benchmark/carbon-nanotube/input.py](benchmark/copper-filament/input.py) #### Case L: Silicon cluster A ground state calculation for a silicon cluster in vacuum. By default the cluster has a radius of 15Å (freely adjustable) and consists of 702 atoms, and ScaLAPACK is used for parallelisation. Expected to scale up to 1000 nodes and/or 10000 MPI tasks. Input file: [benchmark/carbon-nanotube/input.py](benchmark/silicon-cluster/input.py) ### Running the benchmarks No special command line options or environment variables are needed to run the benchmarks on most systems. One can simply say e.g. ``` mpirun -np 256 gpaw-python input.py ``` #### Special case: KNC For KNCs (Xeon Phi Knights Corner), one needs to use a wrapper script to set correct affinities for pyMIC (see [scripts/affinity-wrapper.sh](scripts/affinity-wrapper.sh) for an example) and to set two environment variables for GPAW: ```shell GPAW_OFFLOAD=1 # (to turn on offloading) GPAW_PPN= ``` For example, in a SLURM system, this could be: ```shell GPAW_PPN=12 GPAW_OFFLOAD=1 mpirun -np 256 -bootstrap slurm \ ./affinity-wrapper.sh 12 gpaw-python input.py ``` #### Examples Example [job scripts](scripts/) (`scripts/job-*.sh`) are provided for different systems together with related machine specifications (`scripts/specs.*`) that may offer a helpful starting point.