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 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 Ingredients Install and Configure an Apptainer* Container Run Analysis inside the Container See Also 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 in an Apptainer* Container

Learn how to configure an Apptainer container to run analyses with Intel® VTune™ Profiler. Identify hot spots in an application that is running in the isolated container environment.

Ingredients

This section lists the hardware and software tools used for the performance analysis scenario.

  • Application: MatrixMultiplication

    This Java* application is used as a demo and not available for download.

  • Tools: Intel VTune Profiler

  • Linux container runtime: Apptainer

  • Operating system: Ubuntu* 20.04

  • CPU: Intel® microarchitecture code named Skylake with 8 logical CPUs

Install and Configure an Apptainer* Container

  1. Install Apptainer. For installation instructions, see the Apptainer installation guide.

  2. Create an Apptainer* container, for example, using the Docker Hub: 

    
host> apptainer build ubuntu.img docker://ubuntu:latest
INFO:    Starting build...
Getting image source signatures
Copying blob 5e8117c0bd28 done
Copying config b6548eacb0 done
Writing manifest to image destination
Storing signatures
2023/12/08 15:23:58  info unpack layer: sha256:5e8117c0bd28aecad06f7e76d4d3b64734d59c1a0a44541d18060cd8fba30c50
INFO:    Creating SIF file...
INFO:    Build complete: ubuntu.img
    NOTE:

    Make sure the ubuntu.img file is created in the current directory.

  3. Run the container.

    Apptainer allows you to map directories on your host system to directories within your container. This way, you can read and write data on the host system. For example, if you have a host folder /tmp/vtune with VTune Profiler and a Java application, you must run the container and map /tmp/vtune to /local/vtune within the container. 

    
host> apptainer shell --bind 
/opt/intel/oneapi/vtune/2024.0:/local/vtune --bind 
/test_application:/local/test_application ./ubuntu.img

Run Analysis inside the Container

From the Apptainer container, open the command line interface of VTune Profiler(vtune-cl). Run an analysis for your Java application.

For example, to run the Hotspots analysis on the MatrixMultiplication application, type: 

Apptainer> /local/vtune/bin64/vtune -collect hotspots -- /local/test_application/jdk-21.0.1/bin/java -cp /local/test_application MatrixMultiplication 2000 2000 2000 2000
NOTE:

To profile a target application running in the Apptainer container, you must open VTune Profiler from the same container.

When the analysis result is ready, use the VTune Profiler GUI installed on the host system to open the result. Start in the Summary window to see a performance overview for the application:

NOTE:

If you need to re-finalize an analysis result outside the Apptainer container (for example, in the GUI version of VTune Profiler installed on the host system), ensure that all binary and source files required for module resolution are accessible outside the container.

Parent topic: Configuration Recipes
Level Two Title