WarpX Accelerated Nodes Parallel Computing Paper

This dataset contains the inputs, outputs, job submission scripts, and executables<br> used to create the Figures in "Porting WarpX to GPU-accelerated platforms" by A. Myers<br> et. al, submitted to Parallel Computing as part of the ECP Special Issue on Transitioning<br> to Accelerated nodes. These results were obtained using the October, 2020 release tags of WarpX and AMReX,<br> 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<br> 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<br> Jupyter notebook called "bin_size.ipynb". StrongScaling: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "strong_scaling.ipynb". WeakScalingCPU: contains material used to make Figure 4. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb". WeakScalingGPU: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb". Roofline: contains material used to make the roofline plots (Figures 2 and 3). This<br> includes output generated using nsight-compute with WarpX and python scripts for<br> processing and plotting these output files. These scripts and methodology originally<br> come from Charlene Yang at NERSC. The file "script.sh" was used to generate the<br> profiler output本数据集包含用于复现《Porting WarpX to GPU-accelerated platforms》一文配图的输入数据、输出结果、作业提交脚本与可执行文件。该文由A. Myers等人撰写，已作为ECP转型加速节点专刊的一部分提交至《Parallel Computing》期刊。 本研究结果基于2020年10月版的WarpX与AMReX发布标签，可分别从以下GitHub仓库获取：https://github.com/ECP-WarpX/WarpX 与 https://github.com/AMReX-Codes/amrex。 本次实验在Summit超算平台上加载了以下模块： 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 若需使用Nsight Compute（nsight-compute）生成屋顶线图，还需额外加载nsight-compute/2020.1.2。 数据集清单： - BinScan：包含用于生成图1的相关材料，可通过名为"bin_size.ipynb"的Jupyter 笔记本（Jupyter Notebook）生成该配图。 - StrongScaling：包含用于生成图5的相关材料，可通过名为"strong_scaling.ipynb"的Jupyter 笔记本（Jupyter Notebook）生成该配图。 - WeakScalingCPU：包含用于生成图4的相关材料，可通过名为"weak_scaling.ipynb"的Jupyter 笔记本（Jupyter Notebook）生成该配图。 - WeakScalingGPU：包含用于生成图5的相关材料，可通过名为"weak_scaling.ipynb"的Jupyter 笔记本（Jupyter Notebook）生成该配图。 - Roofline：包含用于生成屋顶线图（图2与图3）的相关材料，其中包含使用WarpX配合Nsight Compute（nsight-compute）生成的输出结果，以及用于处理与可视化这些输出文件的Python脚本。上述脚本与分析方法最初由NERSC的Charlene Yang提供。本次分析使用"script.sh"文件生成性能分析器输出。