Intel® Advisor Help

Offload Modeling Accuracy Levels in Command Line

For each perspective, Intel® Advisor has several levels of collection accuracy. Each accuracy level is a set of analyses and properties that control what data is collected and the level of collection details. The higher accuracy value you choose, the higher runtime overhead is added.

In CLI, each accuracy level corresponds to a set of commands with specific options that you should run one by one to get a desired result.

The following accuracy levels are available:

Comparison / Accuracy Level

Low

Medium

High

Overhead

5 - 10x

15 - 50x

50 - 80x

Goal

Model performance of an application that is mostly compute bound and does not have dependencies

Model application performance considering memory traffic for all cache and memory levels

Model application performance with all potential limitations for offload candidates

Analyses

Survey + Characterization (Trip Counts and FLOP) + Performance Modeling with no assumed dependencies

Survey + Characterization (Trip Counts and FLOP with cache simulation, callstacks, and light data transfer simulation) + Performance Modeling with no assumed dependencies

Survey + Characterization (Trip Counts and FLOP with cache simulation, callstacks, and medium data transfer simulation) + Dependencies + Performance Modeling with assumed dependencies

Result

Basic Offload Modeling report that shows potential speedup and performance metrics estimated on a target considering memory traffic from execution units to L1 cache only. The result may be inaccurate for memory-bound applications.

Offload Modeling report extended with data transfers estimated between host and device platforms considering memory traffic for all cache and memory levels

Offload Modeling report with detailed data transfer estimations and automated check for loop-carried dependencies for more accurate search for the most profitable regions to offload

You can generate commands for a desired accuracy level from the Intel Advisor GUI. See Generate Command Lines from GUI for details.

Note

There is a variety of techniques available to minimize data collection, result size, and execution overhead. Check Minimize Analysis Overhead .

Low Accuracy

To model application performance for Intel® Iris® Xe MAX graphics (gen12_dg1 configuration) with low accuracy, run the perspective as follows:

  1. Run the Survey analysis:

    advisor --collect=survey --project-dir=./advi --stackwalk-mode=online --static-instruction-mix –- myApplication
  2. Run the Characterization analysis to collect trip count and FLOP data:

    advisor --project-dir=./advi --collect=tripcounts --flop --target-device=gen12_dg1 –- myApplication
  3. Run performance modeling:

    advisor --collect=projection --project-dir=./advi --config=gen12_dg1 --no-assume-dependencies

Medium Accuracy

To model application performance for Intel® Iris® Xe MAX graphics (gen12_dg1 configuration) with medium accuracy, run the perspective as follows:

  1. Run the Survey analysis:

    advisor --collect=survey --project-dir=./advi --stackwalk-mode=online --static-instruction-mix –- myApplication
  2. Run the Characterization analysis to collect trip count and FLOP data and estimate data transfers:

    advisor --collect=tripcounts --project-dir=./advi --flop --stacks --target-device=gen12_dg1 --enable-cache-simulation --data-transfer=light –- myApplication
  3. Run performance modeling:

    advisor --collect=projection --project-dir=./advi --config=gen12_dg1 --no-assume-dependencies

High Accuracy

To model application performance for Intel® Iris® Xe MAX graphics (gen12_dg1 configuration) with high accuracy, run the perspective as follows:

  1. Run the Survey analysis:

    advisor --collect=survey  --project-dir=./advi --stackwalk-mode=online --static-instruction-mix -- myApplication
  2. Run the Characterization analysis to collect trip count and FLOP data and estimate data transfers:

    advisor --collect=tripcounts --project-dir=./advi --flop --stacks --target-device=gen12_dg1 --enable-cache-simulation --data-transfer=medium -- myApplication
  3. Run the Dependencies analysis for loops potentially profitable for offloading to a target platform:

    advisor --collect=dependencies --project-dir=./advi --select markup=gpu_generic --loop-call-count-limit=16 --filter-reductions -- myApplication
  4. Run performance modeling:

    advisor --collect=projection --project-dir=./advi --config=gen12_dg1

See Check How Dependencies Affect Modeling for a recommended strategy to check for loop-carried dependencies.

You can view the results in the Intel Advisor GUI, in CLI, or an interactive HTML report.

See Also