Intel VTune Profiler Performance Analysis Cookbook

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ID 766316
Date 5/19/2023
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Document Table of Contents
Document Table of Contents x
Intel® VTune™ Profiler Performance Analysis Cookbook
Intel® VTune™ Profiler Performance Analysis Cookbook x
Methodologies Configuration Recipes Tuning Recipes Notices and Disclaimers
Methodologies x
Top-down Microarchitecture Analysis Method OpenMP* Code Analysis Method 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 Machine Learning 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 Measuring Performance Impact of NUMA in Multi-Processor Systems (NEW) 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 (NEW) 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 a Singularity* Container Ingredients Install and Configure a Singularity* Container Run Analysis inside the 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 Inefficient Synchronization Inefficient TCP/IP Synchronization OS Thread Migration OpenMP* Imbalance and Scheduling Overhead Processor Cores Underutilization: OpenMP* Serial Time Scheduling Overhead in Intel® Threading Building Blocks (Intel® TBB) Apps PMDK Application Overhead
Intel® VTune™ Profiler Performance Analysis Cookbook

Profiling in a Singularity* Container

This recipe guides you through the configuration of a Singularity container for the Intel® VTune™ Profiler analysis to identify hotspots in an application 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

    NOTE:

    • Starting with the 2020 release, Intel® VTune™ Amplifier has been renamed to Intel® VTune™ Profiler.

    • Most recipes in the Intel® VTune™ Profiler Performance Analysis Cookbook are flexible. You can apply them to different versions of Intel® VTune™ Profiler. In some cases, minor adjustments may be required.

    • Get the latest version of Intel® VTune™ Profiler:

  • Linux container runtime: singularity

  • Operating system: Ubuntu* 16.04

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

Install and Configure a Singularity* Container

  1. Install the Singularity software (for example, version 2.4.5): 

    host> VERSION=2.4.5
host> wget https://github.com/singularityware/singularity/releases/download/$VERSION/singularity-$VERSION.tar.gz
host> tar xvf singularity-$VERSION.tar.gz
host> cd singularity-$VERSION
host>./configure --prefix=/usr/local
host> make
host> sudo make install
    NOTE:

    For detailed installation instructions, see https://singularity.lbl.gov/install-linux.

  2. Create a Singularity container, for example, using the Docker Hub: 

    host> singularity build ubuntu.img docker://ubuntu:latest
Docker image path: index.docker.io/library/ubuntu:latest
Cache folder set to /root/.singularity/docker
Importing: base Singularity environment
Importing: /root/.singularity/docker/sha256:d3938036b19cfa369e1081a6776b07b54be9612bc4c8fed7f139370c8142b79f.tar.gz
Importing: /root/.singularity/docker/sha256:a9b30c108bda615dc10e402f62d712f413214ea92c7ec4354cd1cc0f3450bc58.tar.gz
Importing: /root/.singularity/docker/sha256:67de21feec183fcd009a5eddc4de8c346ee0f4369a20047f1a302a90716fc741.tar.gz
Importing: /root/.singularity/docker/sha256:817da545be2ba4bac8f6b4da584bca0fb4844938ecc462b9feab1001b5df7405.tar.gz
Importing: /root/.singularity/docker/sha256:d967c497ce230b63996a7b1fc6ec95b741aea9348118d3328c676f13be789fa7.tar.gz
Importing: /root/.singularity/metadata/sha256:c6a9ef4b9995d615851d7786fbc2fe72f72321bee1a87d66919b881a0336525a.tar.gz
Building Singularity image...
Singularity container built: ubuntu.img
Cleaning up...
    NOTE:

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

  3. Run the container.

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

    host> singularity shell --bind /tmp/vtune:/local/vtune ./ubuntu.img
Singularity: Invoking an interactive shell within container...
Singularity ubuntu.img:~>

Run Analysis inside the Container

From the Singularity container, launch the command line interface of the VTune Profiler, amplxe-cl, and run an analysis for your Java application. For example, to run Advanced Hotspots analysis for the MatrixMultiplication application, enter: 

Singularity ubuntu.img:~> cd /local/vtune/matrix/ 
Singularity ubuntu.img:/local/vtune/matrix> /local/vtune/bin64/vtune -collect advanced-hotspots -- /local/vtune/jdk9.0.1-x64/bin/java -cp . MatrixMultiplication 2000 2000 2000 2000
NOTE:

  • To profile a target application running in the Singularity container, make sure to launch the VTune Profiler from the same container. Running the VTune Profiler outside the container for Singularity profiling is not supported.

  • Advanced Hotspots analysis was integrated into the generic Hotspots analysis starting with VTune Amplifier 2019, and is available via the Hardware Event-based Sampling collection mode.

When the analysis result is collected, you may open it with the GUI version of the VTune Profiler installed on the host system and follow a traditional analysis flow starting with the Summary window that provides an overview of the application performance:

NOTE:

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

Parent topic: Configuration Recipes

See Also

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