Plotting and Interpreting Results
Benchmarking & Scaling Tutorial/Results /
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Revision as of 11:26, 11 March 2022 by Sebastian-potthoff-3c73@uni-muenster.de (talk | contribs)
| Tutorial | |
|---|---|
| Title: | Benchmarking & Scaling |
| Provider: | HPC.NRW
|
| Contact: | tutorials@hpc.nrw |
| Type: | Online |
| Topic Area: | Performance Analysis |
| License: | CC-BY-SA |
| Syllabus
| |
| 1. Introduction & Theory | |
| 2. Interactive Manual Benchmarking | |
| 3. Automated Benchmarking using a Job Script | |
| 4. Automated Benchmarking using JUBE | |
| 5. Plotting & Interpreting Results | |
Weak scaling
We can write a simple Python script to process and plot the resulting data. For this purpose we are making use of the numpy and matplotlib Python libraries.