Difference between revisions of "Benchmarking & Scaling Tutorial/Results"
Benchmarking & Scaling Tutorial/Results
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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. | 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. | ||
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<syntaxhighlight lang="python"> | <syntaxhighlight lang="python"> | ||
#!/usr/bin/env python3 | #!/usr/bin/env python3 | ||
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'''Previous''': [[Benchmarking_%26_Scaling_Tutorial/Automated_Benchmarking | Automated Benchmarking using a Job Script ]] | '''Previous''': [[Benchmarking_%26_Scaling_Tutorial/Automated_Benchmarking | Automated Benchmarking using a Job Script ]] | ||
Revision as of 11:26, 11 March 2022
| 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.