Replication Data for: Analyzing HPC Monitoring Data With a View Towards Efficient Resource Utilization

10.26165/JUELICH-DATA/BDFBPQMaloney, SamuelSamuelMaloney0000-0003-3087-1440JSCSuarez, EstelaEstelaSuarez0000-0003-0748-7264JSCEicker, NorbertNorbertEicker0000-0002-8254-8357JSCGuimarães, FilipeFilipeGuimarães0000-0002-5618-6727JSCFrings, WolfgangWolfgangFrings0000-0002-9463-9264JSCReplication Data for: Analyzing HPC Monitoring Data With a View Towards Efficient Resource UtilizationJülich DATA2024Computer and Information ScienceRuntime; High performance computing; Graphics processing units; Bandwidth; Computer architecture; Supercomputers; Robustness; Hardware; Resource management; Monitoring; HPC; Dynamic/Adaptive scheduling; Predictive analyticsMaloney, SamuelSamuelMaloneyJSC2024-11-292024-11-2910.1109/SBAC-PAD63648.2024.00023317813033068394437426269473998408801381687029653656258862572837689665422836680178127322629355232659application/octet-streamapplication/octet-streamapplication/octet-streamapplication/octet-streamapplication/octet-streamapplication/octet-streamapplication/octet-streamapplication/octet-streamtext/x-pythonapplication/octet-streamapplication/pdftext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/markdown1.0CC0 WaiverReplication Data for the analysis described in the IEEE/SBC SBAC-PAD 2024 paper "Analyzing HPC Monitoring Data With a View Towards Efficient Resource Utilization". Specifically, it contains the data and Python scripts used to generate Figures 2 through 9 in the paper, by computing the relevant cumulative distribution functions (CDFs) or other aggregations (for Figure 4) and then generating the associated plot. The data is contained in parquet files, using zstd compression; see the README file for more details.