Collect dependencies data to predict and eliminate data sharing problems.
Collect memory access patterns data.
Project performance on a target device.
Run the Survey analysis immediately followed by the Trip Counts & FLOP analysis to visualize actual performance against hardware-imposed performance ceilings.
Collect suitability data by executing annotated code to analyze the proposed threading parallelism opportunities and estimate where performance gains are most likely.
Survey the target (your executable application) and collect data about code that may benefit from (more) parallelism.
Collect the following data and add it to the Survey report: loop iteration, floating-point and integer operation, and memory traffic statistics, and more.
$ advisor --collect=survey --project-dir=./advi --search-dir src:r=./src -- ./bin/myApplication
$ advisor --collect=map --mark-up-list=5,10,12 --project-dir=./advi --search-dir src:r=./src -- ./bin/myApplication
$ mpirun -n 4 "advisor --collect=survey --project-dir=./advi" -- <PATH>/mpi-sample/1_mpi_sample_serial
$ advisor --collect=dependencies --project-dir=./advi --loops="loop-height=0,total-time>2" -- ./bin/myApplication