Intel® VTune™ Profiler Performance Analysis Cookbook

Cookbook

  • 2022
  • 10/20/2022
  • Public Content
Contents

Profiling an OpenMP* Offload Application running on a GPU (NEW)

This recipe illustrates how you can build and compile an OpenMP* application offloaded onto an Intel GPU. The recipe also describes how to use
Intel® VTune™
Profiler
 to run analyses with GPU capabilities (HPC Performance Characterization, GPU Offload, and GPU Compute/Media Hotspots) on the OpenMP application and examine results.

Ingredients

Here are the minimum hardware and software requirements for this performance analysis.

Build and Compile the OpenMP Offload Application

On Linux OS:
  1. Set oneAPI environment variables. Run
    setvars.h
    . You can find this script here:
    . /opt/intel/oneapi/setvars.sh
  2. Go to the sample directory. 
    cd <sample_dir>/DirectProgramming/C++/StructuredGrids/iso3dfd_omp_offload
  3. Compile the OpenMP Offload application: 
    
mkdir build; 
cd build;
cmake -DVERIFY_RESULTS=0 -DCMAKE_CXX_FLAGS="-g -mllvm -parallel-source-info=2"  ..
make -j
    This generates an
    src/iso3dfd
     executable.
    To delete the program, type: 
    make clean
    This removes the executable and object files that were created by the
    make
     command.
On Windows OS:
  1. Set oneAPI environment variables. Run
    setvars.bat
    . You can find this script here:
    C:\Program Files (x86)\Intel\ oneAPI \setvars.bat
    .
  2. Open the sample directory: 
    cd <sample_dir>/ DirectProgramming/C++/StructuredGrids/iso3dfd_omp_offload
  3. Compile the OpenMP Offload application: 
    
mkdir build
cd build
icx /Zi -mllvm -parallel-source-info=2 /std:c++17 /EHsc /Qiopenmp /I../include\ /Qopenmp-targets:spir64 /DUSE_BASELINE /DEBUG ..\src\iso3dfd.cpp ..\src\iso3dfd_verify.cpp ..\src\utils.cpp

Run HPC Performance Characterization Analysis on the OpenMP Offload Application

To get a high-level summary of the performance of the OpenMP Offload application, run the HPC Performance Characterization analysis. This analysis type can help you understand how your application utilizes the CPU, GPU, and available memory. You can also see the extent to which your code is vectorized.
For OpenMP offload applications, the HPC Performance Characterization analysis shows you the hardware metrics associated with each of your OpenMP offload regions.
Prerequisites
: Prepare the system to run a GPU analysis. See Set Up System for GPU Analysis.
  1. Open
    VTune
    Profiler
     and click on
    New Project
     to create a project.
  2. On the welcome page, click on
    Configure Analysis
     to set up your analysis.
  3. Select these settings for your analysis.
    • In the
      WHERE
       pane, select
      Local Host
      .
    • In the
      WHAT
       pane, select
      Launch Application
       and specify the
      iso3dfd_omp_offload
       binary as the application to profile.
    • In the
      HOW
       pane, select the
      HPC Performance Characterization
       analysis type from the
      Parallelism
       group in the Analysis Tree.
    Set up HPC Performance Characterization Analysis
  4. Click the
    Start
     button to run the analysis.
Run Analysis from Command Line:
To run the HPC Performance Characterization analysis form the command line:
  • On Linux OS:
    1. Set
      VTune
      Profiler
       environment variables by exporting the script:
      export <
      install_dir
      >/vtune-vars.sh
    2. Run the HPC Performance Characterization analysis:
      vtune -collect hpc-performance -- src/iso3dfd 256 256 256 16 8 64 100
  • On Windows OS:
    1. Set
      VTune
      Profiler
       environment variables by running the batch file:
      <
      install_dir
      >\vtune-vars.bat
    2. Run the HPC Performance Characterization analysis:
      vtune -collect hpc-performance -- iso3dfd.exe 256 256 256 16 8 64 100

Analyze HPC Performance Characterization Data

Start your analysis by examining the
Summary
 pane. Look at the
Effective Physical Core Utilization
 (or
Effective Logical Core Utilization
) and
GPU Utilization when Busy
 sections to see highlighted issues, if any.
In the
GPU Utilization when Busy
 section, look at the top OpenMP offload regions sorted by offload time spent in those regions. You can see GPU utilization in each of these offload regions.
GPU Utilization When Busy metric
If you compiled your application with the full set of debug information, the names of the regions will contain their source locations. This includes:
  • Name of the function
  • Name of the source file
  • Line number
Generate a Summary Report From the Command Line
To generate a summary report from the command line, type: 
vtune -report summary -r <result>
In this example, the offload activity is classified almost entirely as
Compute
 activity. Also, a single offload region consumed the majority of offload time. Click on its name to switch to the
Bottom-Up
 view. Examine the grouping table with OpenMP offload region durations, region instance counts, and metrics for GPU and CPU.
Hover over the region markers at the top of the timeline view. You can see the name and duration of each offload region and offload operation within that region. The GPU metrics in the timeline help you understand how every instance of an offload region behaves over time.
Generate a Hotspots Report Grouped by Offload Region From the Command Line
To generate a Hotspots report (grouped by offload region) from the command line, type: 
vtune -report hotspots -group-by=offload-region -r <result>
These details establish clearly that GPU activity played an important role in the performance of this application. Next, let us move to the GPU Offload Analysis to learn more.

Run GPU Offload Analysis on the OpenMP Offload Application

Prerequisites
: If you have not already done so, prepare the system to run a GPU analysis. See Set Up System for GPU Analysis.
  1. From the Analysis Tree, select the
    GPU Offload
     analysis type from the
    Accelerators
     group.
  2. Select these settings for your analysis:
    Configuration for GPU Offload Analysis
  3. Click the
    Start
     button to run the analysis.
Run Analysis from Command Line:
To run the GPU Offload analysis form the command line:
  • On Linux OS, type: 
     vtune -collect gpu-offload - src/iso3dfd 256 256 256 16 8 64 100
  • On Windows OS, type: 
     vtune -collect gpu-offload - iso3dfd.exe 256 256 256 16 8 64 100

Analyze GPU Offload Analysis Data

Start your analysis with the
GPU Offload
 viewpoint.
In the
Summary
 window, see statistics on CPU and GPU resource usage. Use this data to determine if your application is:
  • GPU-bound
  • CPU-bound
  • Utilizing the compute resources of your system inefficiently
Families of Intel® X
e
 graphics products starting with Intel® Arc™ Alchemist (formerly DG2) and newer generations feature GPU architecture terminology that shifts from legacy terms. For more information on the terminology changes and to understand their mapping with legacy content, see GPU Architecture Terminology for Intel® X
e
 Graphics.
In this example, the application should use the GPU for intensive computation. However, the result summary informs that GPU usage is actually low.
GPU Usage Metrics for OpenMP Application
Switch to the
Platform
 window. Here, you can see basic CPU and GPU metrics that help analyze GPU usage on a software queue. This data is correlated with CPU usage on the timeline.
Platform View with GPU Utilization Metrics
The information in the
Platform
 window can help you make some inferences.
GPU Bound Applications
CPU Bound Applications
The GPU is busy for a majority of the profiling time.
The CPU is busy for a majority of the profiling time.
There are small idle gaps between busy intervals.
There are large idle gaps between busy intervals.
The GPU software queue is rarely reduced to zero.
Most applications may not present obvious situations as described here. A detailed analysis is important to understand all dependencies. For example, GPU engines that are responsible for video processing and rendering are loaded in turns. In this case, they are used in a serial manner. When the application code runs on the CPU, this can cause an ineffective scheduling on the GPU. The behavior can mislead you to interpret the application to be GPU bound.
Identify the GPU execution phase based on the computing task reference and
GPU Utilization
 metrics. Then, you can define the overhead for creating the task and placing it into a queue.
To investigate a computing task, switch to the
Graphics
 window to examine the type of work (rendering or computation) running on the GPU per thread. Select the
Computing Task
 grouping and use the table to study the performance characterization of your task.
Generate a Hotspots Report Grouped by Computing Task From the Command Line
To generate a Hotspots report (grouped by computing task) from the command line, type: 
vtune -report hotspots -group-by=computing-task -r <result>
Use the README file in the sample to profile other implementations of
iso3dfd_omp_offload code
.
In the next section, continue your investigation with the GPU Compute/Media Hotspots analysis.

Run GPU Compute/Media Hotspots Analysis on the OpenMP Offload Application

Prerequisites
: If you have not already done so, prepare the system to run a GPU analysis. See Set Up System for GPU Analysis.
To run the analysis:
  1. In the
    Accelerators
     group, select the
    GPU Compute/Media Hotspots
     analysis type.
  2. Configure analysis options as described in the previous section.
  3. Click the
    Start
     button to run the analysis.
Run Analysis from Command Line
To run the analysis from the command line:
  • On Linux OS:
    vtune -c gpu-hotspots -knob profiling-mode=source-analysis - src/iso3dfd 256 256 256 16 8 64 100
  • On Windows OS:
    vtune -collect gpu-hotspots - iso3dfd.exe 256 256 256 16 8 64 100

Analyze Your Compute Task

The default analysis configuration invokes the
Characterization
 profile with the Overview metric set. In addition to individual compute task characterization that is available through the
GPU Offload
 analysis,
VTune
Profiler
 provides memory bandwidth metrics that are categorized by different levels of GPU memory hierarchy.
Grouping by Computing Task
For a visual representation of the memory hierarchy, see the
Memory Hierarchy Diagram
. This diagram reflects the microarchitecture of the current GPU and shows memory bandwidth metrics. Use the diagram to understand the data traffic between memory units and execution units. You can also identify potential bottlenecks that cause EU stalls.
Memory Hierarchy Diagram
You can also analyze compute tasks at the source code level. For example, you can count GPU clock cycles spent on a particular task or due to memory latency. Use the
Source Analysis
 option for this purpose.
Run Memory Latency Source Analysis from Command Line
To run the analysis with the Memory Latency Source Analysis option from the command line:
  • On Linux OS:
    vtune -c gpu-hotspots -knob profiling-mode=source-analysis -knob source-analysis=mem-latency -r iso_ghs_src-analysis_mem - src/iso3dfd 256 256 256 16 8 64 100
In the source view, examine the
Average Latency Cycles
 for the offload kernel.
Memory Latency Source Analysis
Generate a Hotspots Report With Source for a Computing Task From the Command Line
To generate a Hotspots report (with source for a computing task) from the command line, type: 
vtune -report hotspots -source-object 'computing-task=Iso3dfdIteration$omp$offloading:50' -group-by=gpu-source-line -r <result>
Run Basic Block Latency Source Analysis from Command Line
To run the analysis with the Basic Blocks Latency Source Analysis option from the command line:
  • On Linux OS:
    vtune -c gpu-hotspots -knob profiling-mode=source-analysis -r iso_ghs_src-analysis - src/iso3dfd 256 256 256 16 8 64 100
In the source view, examine the
Average Latency Cycles
 for the offload kernel.
Basic Blocks Latency Source Analysis
Discuss this recipe in the
VTune
Profiler
 developer forum.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.