Collects Survey data with basic execution metrics of your application
Runs Characterization analysis to get the information about Trip Counts and floating-point operations (FLOP), simulate cache traffic, and estimate time required to transfer data from one device to another
Models application performance on a target device assuming that main hotspots can be executed in parallel

Information on total potential speed-up of your application
Top 5 offloaded code regions in your call tree
Top 5 regions that are not profitable to offload
Number of offloaded functions/loops
Fraction of offloaded code relative to total time of original application

Run the Survey analysis and collect performance metrics with online stack walk and static instruction mix on a host device:
advisor --collect=survey --stackwalk-mode=online --static-instruction-mix --project-dir=./advi -- myApplication
Run the Trip Counts and FLOP analysis, simulate multi-level GPU memory subsystem behavior, and estimate time required for transferring data from host to target:
advisor --collect=tripcounts --flop --stacks --enable-cache-simulation --data-transfer=light --target-device=gen9_gt2 --project-dir=./advi -- myApplication
Where:
flop
collects data about floating-point operations, integer operations, memory traffic, and mask utilization metrics.
stacks
performs advanced collection of call stack data.
enable-cache-simulation
models memory subsystem behavior on a target application.
data-transfer
is set to light mode that models data transfer between host and device memory.
target-device
specifies a device configuration to use for simulating cache behavior during Trip Counts collection. The following device configurations are available:
xehp_sdv
|
xehp_sdv_384eu
|
gen12_tgl
|
gen12_dg1
|
gen11_icl
|
gen9_gt4
|
gen9_gt3
|
gen9_gt2
.
Model application performance on a target device:
advisor --collect=projection --no-assume-dependencies --config=gen9_gt2 --project-dir=./advi
Where:
no-assume-dependencies
assumes that a loop does not have dependencies if the dependency type is unknown. It’s recommended option for the Medium accuracy mode since dependency analysis is not executed.
config
sets a device configuration to model your application performance for. By default, this option is set to
gen11_icl
. The following device configurations are available:
xehp_sdv
|
xehp_sdv_384eu
|
gen12_tgl
|
gen12_dg1
|
gen11_icl
|
gen9_gt4
|
gen9_gt3
|
gen9_gt2
.

advisor --snapshot --project-dir=./advi --pack --cache-sources --cache-binaries -- /tmp/my_proj_snapshot

Rerun Performance Modeling analysis assuming that your main hotspots with unknown dependency types cannot be executed in parallel:
In the

Intel Advisor

GUI, expand the Performance Modeling analysis, make sure to enable the Assume Dependencies check box, and click the button to run it.

In the CLI, use the following command line:

advisor --collect=projection --assume-dependencies --config=gen9_gt2 --project-dir=./advi
If the difference between metrics for your hotspots collected with and without Assume Dependencies option is small (for example, 2x speed-up with Assume Dependencies and 2.2x speed-up without Assume Dependencies), rely on collected results. If the difference is big (for example 2x speed-up with Assume Dependencies and 50x speed-up without Assume Dependencies), consider running Dependencies analysis.

Intel Advisor Cookbook: Model C++ Application Performance on a Target GPU
Identify Code Regions to Offload to GPU and Visualize GPU Usage