[x] Login to the cluster with enabled X-forwarding and start the VTune GUI. Create a new project for your application. Add an analysis to the project but do not start the analysis on the login node. Instead, allocate a node and run the analysis on this node.
Note: Not all analysis types are usable since respective kernel drivers are not installed on the compute nodes. However, Hotspots and Threading are a good starting point.
[x] Now, run the same analysis through the command line tool in a batch job! You can copy & paste the required command from the GUI. Remember to load the Intel tools in the job script first.
[x] Once your batch job has finished, use the GUI to visualize the results. Add debug symbols to the code, i.e., use -g. Consider to disable function inlining to get finer results: -fno-inline.
[x] Which parts are compute-intensive? Did you expect this?
[x] Think about how you could improve the performance of your code, especially the compute-intensive parts. Consider features of C++, e.g., inlining or templates, as well as algorithmic features, e.g., the reuse of calculated values, avoidance of unnecessary square roots or divisions!
[ ] (optional) Instrument your code manually using Score-P. Use Cube for the visualization of your measurements. Use Score-P’s PAPI-interface to access hardware counters.
Note: Not all analysis types are usable since respective kernel drivers are not installed on the compute nodes. However, Hotspots and Threading are a good starting point.
[x] Now, run the same analysis through the command line tool in a batch job! You can copy & paste the required command from the GUI. Remember to load the Intel tools in the job script first.
[x] Once your batch job has finished, use the GUI to visualize the results. Add debug symbols to the code, i.e., use -g. Consider to disable function inlining to get finer results: -fno-inline.
[x] Which parts are compute-intensive? Did you expect this?
[x] Think about how you could improve the performance of your code, especially the compute-intensive parts. Consider features of C++, e.g., inlining or templates, as well as algorithmic features, e.g., the reuse of calculated values, avoidance of unnecessary square roots or divisions!
[ ] (optional) Instrument your code manually using Score-P. Use Cube for the visualization of your measurements. Use Score-P’s PAPI-interface to access hardware counters.