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System Performance Metrics (Jülich Supercomputing Centre)
Replication Data for: Analyzing HPC Monitoring Data With a View Towards Efficient Resource Utilization
Replication Data for: Analyzing HPC Monitoring Data With a View Towards Efficient Resource Utilization
Version 1.0
Maloney, Samuel; Suarez, Estela; Eicker, Norbert; Guimarães, Filipe; Frings, Wolfgang, 2024, "Replication Data for: Analyzing HPC Monitoring Data With a View Towards Efficient Resource Utilization", https://doi.org/10.26165/JUELICH-DATA/BDFBPQ, Jülich DATA, V1
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Description
Replication 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.
Subject Computer and Information Science
Keyword Runtime; High performance computing; Graphics processing units; Bandwidth; Computer architecture; Supercomputers; Robustness; Hardware; Resource management; Monitoring; HPC; Dynamic/Adaptive scheduling; Predictive analytics
Related Publication S. Maloney, E. Suarez, N. Eicker, F. Guimarães, and W. Frings, “Analyzing HPC monitoring data with a view towards efficient resource utilization,” in 2024 IEEE 36th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Hilo, HI, USA, Nov. 13–15, 2024, pp. 170–181, https://doi.org/10.1109/SBAC-PAD63648.2024.00023 doi: 10.1109/SBAC-PAD63648.2024.00023
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__init__.py
sbac-pad-2024-artifacts-main/utils/
Python Source Code - 2.5 KB - Nov 29, 2024 - 0 Downloads
SHA-256: da258e7666f961756ac46c30075b6a161cf1949e98b431a050f4422e8b86424c
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__init__.cpython-312.pyc
sbac-pad-2024-artifacts-main/utils/__pycache__/
Unknown - 3.6 KB - Nov 29, 2024 - 0 Downloads
SHA-256: b97dbe8fe02df74f9dbd28a4a9a866366740b7464be2594eb67988a8711dadc6
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__init__.cpython-311.pyc
sbac-pad-2024-artifacts-main/utils/__pycache__/
Unknown - 2.9 KB - Nov 29, 2024 - 0 Downloads
SHA-256: f15a9e290757bd282d4c9e10cdb684bd9c2c265f093806f218ff3f5fd9df10f0
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README.md
sbac-pad-2024-artifacts-main/
Markdown Text - 2.6 KB - Nov 29, 2024 - 0 Downloads
SHA-256: 2117ce0874c6f349dabe22dd723fba1fa5e224a7fea78e097b6dcdefd92929dc
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plot_mem_intensity.py
sbac-pad-2024-artifacts-main/
Python Source Code - 5.4 KB - Nov 29, 2024 - 0 Downloads
SHA-256: 915c7840f3ae45b47ffeae28598cba82fe8e9af11e4f83bd7fcb2d1ab16796eb
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plot_cdf_sizes.py
sbac-pad-2024-artifacts-main/
Python Source Code - 6.1 KB - Nov 29, 2024 - 0 Downloads
SHA-256: 829a57774a5d185deb6187b7c2cc49f7ad4e970c85c885f8794d30843bb9981f
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plot_cdf_mem.py
sbac-pad-2024-artifacts-main/
Python Source Code - 7.2 KB - Nov 29, 2024 - 0 Downloads
SHA-256: 73fd7f86d87c2f191a47ac43f12357436c36303724df042e89b4ddce2ee573f4
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plot_cdf_IC.py
sbac-pad-2024-artifacts-main/
Python Source Code - 7.6 KB - Nov 29, 2024 - 0 Downloads
SHA-256: d1bdd645dcae8b3b6bdd5f8fce1bffc68f44811e2d3b241e6ae104683e8e4896
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plot_cdf_gpu_power.py
sbac-pad-2024-artifacts-main/
Python Source Code - 2.8 KB - Nov 29, 2024 - 0 Downloads
SHA-256: 14d4f617e66881731e49ef5c499cc792737e09639ab910f628ae9ff63f75b3c7
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plot_cdf_gpu.py
sbac-pad-2024-artifacts-main/
Python Source Code - 6.6 KB - Nov 29, 2024 - 0 Downloads
SHA-256: aed9a3390c229aa4db56a6c25feaf0381e640c8de6a737b9dc03bb6e88d3eb71
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