Intel® Advisor Help

Run GPU-to-GPU Performance Modeling from Command Line

To model performance of a Data Parallel C++ (DPC++), OpenCL™, or OpenMP* target application on a graphics processing unit (GPU) device, run the GPU-to-GPU modeling workflow of the Offload Modeling perspective.

Workflow

The GPU-to-GPU performance modeling workflow is similar to the CPU-to-GPU modeling workflow and includes the following steps:

  1. Measure the performance of GPU-enabled kernels running on an Intel® Graphics.
  2. Model application performance on a target GPU device and compare the estimated performance metrics to the baseline performance metrics.

Note

For correct memory object tracing, GPU kernels should run with the oneAPI Level Zero back end.

Prerequisites

  1. Configure your system to analyze GPU kernels.
  2. Set Intel® Advisor environment variables with an automated script to enable Intel Advisor command line interface.

Run the GPU-to-GPU Performance Modeling

To run the GPU-to-GPU performance modeling from command line, you can use one of the following methods:

You can also run the GPU-to-GPU Offload Modeling from Intel Advisor graphical user interface (GUI). See Run Offload Modeling Perspective from GUI.

Tip

If you want to analyze an MPI application, you can generate pre-configured command lines, copy them, and run one by one. For details, see Optional: Generate pre-Configured Command Lines

Method 1. Use Collection Preset

To run the collection preset for the GPU-to-GPU modeling, use the --gpu option with the --collect=offload action. When you run the collection, it sequentially runs data collection on a GPU and performance modeling steps. The specific analyses and options depend on the accuracy level you specify for the collection.

Info: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.

For example, to run the GPU-to-GPU modeling with the default (medium) accuracy level:

The collection progress and commands for each analysis executed will be printed to a terminal or a command prompt. When the collection is finished, you will see the result summary.

You can also specify a different accuracy level to change analyses to run and their options. Available accuracy levels are low, medium (default), and high.

For example, run the high accuracy level:

advisor --collect=offload --accuracy=high --gpu --project-dir=./advi_results -- ./myApplication

If you want to see the commands that are executed at each accuracy level, you can run the collection with the --dry-run and --gpu options. The commands will be printed to a terminal or a command prompt.

For details about each accuracy level, see Offload Modeling Accuracy Levels in Command Line.

Method 2. Use per-Analysis Collection

You can collect data and model performance for your application by running each Offload Modeling analysis in a separate command for more advanced customization. To enable the GPU-to-GPU modeling, use the --profile-gpu option for each analysis you run.

Consider the following workflow example. Using this example, you can run the Survey, Trip Counts, and FLOP analyses to profile an application running on a GPU and the Performance Modeling to model its performance on Intel® Iris® Xe graphics (gen12_tgl configuration) target device.

Info: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.

On Linux OS:

  1. Run the Survey analysis.
    advisor --collect=survey --profile-gpu --project-dir=./advi_results -- ./myApplication
  2. Run the Trip Counts and FLOP analyses with data transfer.
    advisor --collect=tripcounts --profile-gpu --flop --target-device=gen12_tgl --data-transfer=light --project-dir=./advi_results -- ./myApplication
  3. Run the Performance Modeling analysis.
    advisor --collect=projection --profile-gpu --config=gen12_tgl --project-dir=./advi_results

    You will see the result summary printed to the command prompt.

On Windows OS:

  1. Run the Survey analysis.
    advisor --collect=survey --project-dir=.\advi_results -- myApplication.exe
  2. Run the Trip Counts and FLOP analyses with data transfer.
    advisor --collect=tripcounts --profile-gpu --flop --target-device=gen12_tgl --data-transfer=light --project-dir=.\advi_results -- myApplication.exe
  3. Run the Performance Modeling analysis.
    advisor --collect=projection --profile-gpu --config=gen12_tgl --project-dir=.\advi_results

    You will see the result summary printed to the command prompt.

Method 3. Use Python* Scripts

Intel Advisor has three scripts that use the Intel Advisor Python* API to run the Offload Modeling - run_oa.py, collect.py, analyze.py. You can run the scripts with the advisor-python command line interface of the Intel Advisor or with your local Python 3.6 or 3.7.

To enable the GPU-to-GPU modeling, use the --gpu option for each script you run.

Note

The scripts do not support the analysis of MPI applications. For an MPI application, use the per-analysis collection with the Intel Advisor CLI.

You can run the Offload Modeling using different combinations of the scripts and/or the Intel Advisor CLI. For example:

Consider the following examples of some typical scenarios with Python scripts.

Info: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.

Example 1. Run the run_oa.py script to profile an application on GPU and model its performance for Intel® Iris® Xe graphics (gen12_tgl configuration).

You will see the result summary printed to the command prompt.

Example 2. Run the collect.py to profile an application on GPU and run the analyze.py to model its performance.

View the Results

Once the Intel Advisor finishes the analyses, the result is available in the following formats:

For example, the result in the Intel Advisor GUI looks as follows:

GPU-to-GPU Offload Modeling Summary in GUI

See Explore Performance Gain from GPU-to-GPU Modeling for details.

See Also