Intel® VTune™ Profiler

Cookbook

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ID 766316
Date 10/31/2024
Public
Document Table of Contents
Document Table of Contents x
Cookbook
Cookbook x
Methodologies Configuration Recipes Tuning Recipes Notices and Disclaimers
Methodologies x
Analyzing Uncore Perfmon Events for Use of Intel® Data Direct I/O Technology in Intel® Xeon® Processors (NEW) Top-down Microarchitecture Analysis Method OpenMP* Code Analysis Method Custom Data Collection for Performance Analysis (NEW) Software Optimization for Intel® GPUs (NEW) Core Utilization in DPDK Apps PCIe Traffic in DPDK Apps DPDK Event Device Profiling Effective Utilization of Intel® Data Direct I/O Technology Compile a Portable Optimized Binary with the Latest Instruction Set
Configuration Recipes x
Profiling Large Language Models on Intel® Core™ Ultra 200V (NEW) Profiling Applications in Performance Monitoring Unit (PMU)-Enabled Google Cloud* Virtual Machines (NEW) Profiling OpenVINO™ Applications (NEW) Analyzing Uncore Perfmon Events for Use of Intel® Data Direct I/O Technology in Intel® Xeon® Processors (NEW) Profiling High Bandwidth Memory Performance on Intel® Xeon® CPU Max Series (NEW) Profiling Windows* Applications for Hybrid CPU Platforms (NEW) Viewing Analysis Results on a Web Browser (NEW) Profiling Data Parallel Python* Applications (NEW) Profiling Single-Node Kubernetes* Applications (NEW) Analyzing Hot Code Paths Using Flame Graphs (NEW) Improving Hotspot Observability in a C++ Application Using Flame Graphs Profiling Games built with Unity* (NEW) Profiling Games built with Unreal Engine* (NEW) Profiling Java Applications as a Remote User (NEW) Profiling JavaScript* Code in Node.js* Analyzing CPU and FPGA (Intel® Arria® 10 GX) Interaction Profiling a .NET* Core Application Profiling Applications in Amazon Web Services* (AWS) EC2 Instances Enabling Performance Profiling in GitLab* CI Configuring a Hyper-V* Virtual Machine for Hardware-Based Hotspots Analysis Profiling an Application for Performance Anomalies (NEW) Profiling an OpenMP* Offload Application running on a GPU Profiling a SYCL* Application running on a GPU Profiling an FPGA-driven SYCL* Application Ingredients Install and Configure the Toolkit Build the Sample Application Run CPU/FPGA Interaction Analysis Analyze Results See Also Profiling Hardware Without Intel Sampling Drivers Profiling MPI Applications Profiling Docker* Containers Profiling a Remote Target Through a Proxy Server (NEW) Profiling in an Apptainer* Container Profiling Linux*, Android*, and QNX* System Boot Time Using Intel® VTune™ Profiler Server with Visual Studio Code and Intel® DevCloud for oneAPI (NEW) Using Intel® VTune™ Profiler Server in HPC Clusters Using the Command-Line Interface to Analyze the Performance of a SYCL* Application running on a GPU (NEW)
Tuning Recipes x
Cache-Related Latency Issues in Segmented Cache Environment False Sharing Frequent DRAM Accesses Poor Port Utilization Page Faults Instruction Cache Misses OS Thread Migration OpenMP* Imbalance and Scheduling Overhead Processor Cores Underutilization: OpenMP* Serial Time Scheduling Overhead in an Intel® oneAPI Threading Building Blocks Application PMDK Application Overhead
Cookbook

Profiling an FPGA-driven SYCL* Application

Use this recipe to profile an FPGA-driven SYCL application. The recipe features the AOCL Profiler integrated in the CPU/FPGA Interaction (preview) analysis type in Intel® VTune™ Profiler.

Ingredients

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

Install and Configure the Toolkit

  1. Plug the Intel PAC card into the PCIe slot on the machine.

  2. Download and install Intel oneAPI Base Toolkit for Linux. Select all default options and either the online or offline installer.

  3. Download Intel FPGA Add-on for oneAPI Base Toolkit.

  4. Unzip the FPGA add-on package and run setup.sh. Select all default options.

  5. Set up the oneAPI environment. 

    source <oneAPI-install-dir>/setvars.sh

  6. Install the FPGA board. 

    aocl install

  7. Run the diagnose command to ensure that all diagnostics pass. 

    aocl diagnose

Build the Sample Application

  1. Download code samples from the repository for Intel oneAPI DPC++ Compiler samples. 

    git clone https://github.com/intel/BaseKit-code-samples.git

  2. Open the crr sample folder. 

    cd BaseKit-code-samples/FPGAExampleDesigns/crr

  3. Open the src/CMakeLists.txt file.

  4. Locate the line of code that lists hardware flags. It should start with set(HARDWARE_LINK_FLAGS.

  5. Add -Xsprofile to the set of flags.

  6. Go back to the main directory for the sample. Create a new folder called build and open it. 

    mkdir build
cd build

  7. Compile the sample. 

    cmake ..
make fpga
    This process can take several hours. Once it has finished, you should have an executable file called crr.fpga.

You can now run crr.fpga on FPGA hardware.

Run CPU/FPGA Interaction Analysis

  1. Open Intel® VTune™ Profiler and click New Project on the Welcome screen.

    The Create a Project dialog box opens.

  2. Specify a project name, a location for your project, and click Create Project.

    The Configure Analysis window opens.

  3. In the WHERE pane, select Local Host.

  4. In the WHAT pane, select Launch Application as the target.

    • In the Application field, specify the path to the crr.fpga executable.

    • In the Application parameters field, enter ordered_inputs.csv.

    Set up FPGA analysis

  5. In the HOW pane, select CPU/FPGA Interaction (preview) from the Platform Analysis group.

  6. In the analysis settings, select AOCL Profiler for the FPGA profiling data source.

    Set up FPGA analysis
  7. Click Start at the bottom to run the analysis.

Analyze Results

Once data collection completes, you can see the finalized results in the CPU/FPGA Interaction viewpoint. Start with the Summary window to view these details:

  • FPGA top compute tasks

  • Top tasks and hotspots for the CPU

Result summary for CPU/FPGA Interaction

Switch to the Bottom-up window to see detailed information at the kernel level including:

  • Stalls
  • Occupancy
  • Data transfer size
  • Average bandwidth for transferred data

Bottom-up window

Use the timeline view to see these details about kernel instances:

  • Start/end times
  • Overtime stalls
  • Occupancy
  • Bandwidth metrics

In the Bottom-up window, right-click on a kernel and select View Source from context menu.

This opens the Source View, where you can see metrics for specific kernel source lines.

Parent topic: Configuration Recipes
Level Two Title