WarpX Accelerated Nodes Parallel Computing Paper (Q6716910)

This dataset contains the inputs, outputs, job submission scripts, and executables used to create the Figures in Porting WarpX to GPU-accelerated platforms by A. Myers et. al, submitted to Parallel Computing as part of the ECP Special Issue on Transitioning to Accelerated nodes. These results were obtained using the October, 2020 release tags of WarpX and AMReX, available on Github here: https://github.com/ECP-WarpX/WarpX and here: https://github.com/AMReX-Codes/amrex The following module files were loaded on Summit: 1) hsi/5.0.2.p5 2) xalt/1.2.0 3) lsf-tools/2.0 4) darshan-runtime/3.1.7 5) DefApps 6) cuda/10.1.243 7) gcc/6.4.0 8) spectrum-mpi/10.3.1.2-20200121 To use nsight-compute for the roofline plots, we also loaded: nsight-compute/2020.1.2 Manifest: BinScan: contains material used to make Figure 1. To generate the figure, use the Jupyter notebook called bin_size.ipynb. StrongScaling: contains material used to make Figure 5. To generate the figure, use the Jupyter notebook called strong_scaling.ipynb. WeakScalingCPU: contains material used to make Figure 4. To generate the figure, use the Jupyter notebook called weak_scaling.ipynb. WeakScalingGPU: contains material used to make Figure 5. To generate the figure, use the Jupyter notebook called weak_scaling.ipynb. Roofline: contains material used to make the roofline plots (Figures 2 and 3). This includes output generated using nsight-compute with WarpX and python scripts for processing and plotting these output files. These scripts and methodology originally come from Charlene Yang at NERSC. The file script.sh was used to generate the profiler output