Intel® VTune™ Profiler

Cookbook

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ID 766316
Date 12/20/2024
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 Artificial Intelligence Applications (NEW) Profiling Data Parallel Python* Applications (NEW) Profiling OpenVINO™ Applications (NEW) Profiling Large Language Models on Intel® Core™ Ultra 200V (NEW) Profiling Applications in Performance Monitoring Unit (PMU)-Enabled Google Cloud* Virtual Machines (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 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* Ingredients Enable Support for VTune Profiler in Node.js Profile JavaScript Code Running 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 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 JavaScript* Code in Node.js*

This recipe provides configuration steps to rebuild Node.js* and run a performance analysis of your JavaScript code using Intel® VTune™ Profiler. This analysis includes mixed-mode call stacks that contain JS frames and native frames.

Ingredients

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

  • Application: sample.js. This application is used as a demo. It is not available for download.

  • JavaScript environment: Node.js version 20.4.0 with Chrome* V8 version11.3.244.8

  • Performance analysis tools:Intel® VTune™ Profiler version 2023.1 - Advanced Hotspots analysis with User-Mode Sampling

    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:

  • Operating system: Windows* 10

Enable Support for VTune Profiler in Node.js

  1. Download the most recent node.js sources.

  2. Run the vcbuild.bat script from the root node-v20.4.0 folder: 

    vcbuild.bat enable-vtune

    This script builds Node.js with support for VTune Profiler to profile JavaScript code.

Profile JavaScript Code Running in Node.js

This recipe uses a sample JavaScript application: 

function say(word) {
console.log("Calculating ...");
var res = 1;
for (var i = 0; i < 40000; i++) {
  for (var j = 0; j < 40000; j++) {
    res = ( res + i * res - j )/ 2;
  }
}
console.log("Done.");
console.log(word);
}
 
function execute(someFunction, value) {
  someFunction(value);
}
 
execute(say, "Hello from Node.js!");
To profile this application with VTune Profiler:

  1. Run VTune Profiler from the command line: 

    vtune-gui.exe

  2. Click the New Project icon in the toolbar to create a new project.

  3. In the Launch Application section,

    • Specify node.exe in the Application field

    • Set sample.js in the Application parameters field.

    • To translate frames interpreted by JavaScript, set --interpreted-frames-native-stack as an option in the Application parameters field.

  4. In the Analysis Type pane, select the Hotspots analysis type.

  5. Click Start to run the analysis.

Once the analysis finishes, see results in the Summary window. We can see that the say function took the most CPU time to execute. Click on this function and switch to the Bottom-up view. See the stack flow for the hotspot:

Doubleclick on the say function to run a source level analysis.

Parent topic: Configuration Recipes
Level Two Title