Intel® VTune™ Profiler

User Guide

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ID 766319
Date 3/22/2024
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Document Table of Contents
Document Table of Contents x
User Guide
User Guide x
Introduction Install Intel® VTune™ Profiler Open Intel® VTune™ Profiler Set Up Project Set Up Analysis Target Analyze Performance Intel® VTune™ Profiler Command Line Interface API Support Troubleshooting Reference Notices and Disclaimers
Introduction x
Tuning Methodology Tutorials and Samples Notational Conventions Get Help Product Website and Support Related Information
Install Intel® VTune™ Profiler x
Sampling Drivers Set Up System for GPU Analysis Verify Intel® VTune™ Profiler Installation Install VTune Profiler Server Security Best Practices
Set Up System for GPU Analysis x
Rebuild and Install the Kernel for GPU Analysis Rebuild and Install Module i915 for GPU Analysis on CentOS* Rebuild and Install Module i915 for GPU Analysis on Ubuntu*
Install VTune Profiler Server x
Set Up Transport Security Configure User Authentication/Authorization
Open Intel® VTune™ Profiler x
Get Started with Intel® VTune™ Profiler Intel® VTune™ Profiler Graphical User Interface Web Server Interface Microsoft Visual Studio* Integration Eclipse* and Intel System Studio IDE Integration Containerization Support
Containerization Support x
Run VTune Profiler in a Container Profile Container Targets from the Host
Set Up Project x
WHERE: Analysis System WHAT: Analysis Target HOW: Analysis Types Search Directories
WHERE: Analysis System x
Analysis System Options
WHAT: Analysis Target x
Analysis Target Options
Search Directories x
Search Order
Set Up Analysis Target x
Prepare Application for Analysis Windows* Targets Linux* Targets Embedded Linux* Targets FreeBSD* Targets QNX* Targets Managed Code Targets Android* Targets Intel® Xeon Phi™ Processor Targets Targets in Virtualized Environments Targets in a Cloud Environment Arbitrary Targets Embedded System Targets
Windows* Targets x
Install the Sampling Drivers for Windows* Targets Debug Information for Windows* Application Binaries Compiler Switches for Performance Analysis on Windows* Targets Debug Information for Windows* System Libraries Add Administrative Privileges
Linux* Targets x
Build and Install the Sampling Drivers for Linux* Targets Debug Information for Linux* Application Binaries Compiler Switches for Performance Analysis on Linux* Targets Enable Linux* Kernel Analysis Resolution of Symbol Names for Linux-Loadable Kernel Modules Analyze Statically Linked Binaries on Linux* Targets Set Up Remote Linux* Target
Set Up Remote Linux* Target x
Set Up Linux* System for Remote Analysis Configure SSH Access for Remote Collection Search Directories for Remote Linux* Targets Temporary Directory for Performance Results on Linux* Targets
Embedded Linux* Targets x
Configure Yocto Project* and VTune Profiler with the Integration Layer Configure Yocto Project*/Wind River* Linux* and Intel® VTune™ Profiler with the Intel System Studio Integration Layer Configure Yocto Project* and Intel® VTune™ Profiler with the Linux* Target Package
FreeBSD* Targets x
Set Up FreeBSD* System
Managed Code Targets x
.NET* Targets Windows Store Application Targets Go* Application Targets
Android* Targets x
Build and Install Sampling Drivers for Android* Targets Set Up Android* System Enable Java* Analysis on Android* System Prepare an Android* Application for Analysis Analyze Unplugged Devices Search Directories for Android* Targets
Targets in Virtualized Environments x
Profile Targets on a VMware* Guest System Profile Targets on a Parallels* Guest System Profile Targets on a KVM* Guest System Profile Targets on a Xen* Virtualization Platform Profile Targets in the Hyper-V* Environment
Profile Targets on a KVM* Guest System x
Profile KVM Kernel Modules from the Host Profile KVM Kernel and User Space on the KVM System Profile KVM Kernel and User Space from the Host
Analyze Performance x
User-Mode Sampling and Tracing Collection Hardware Event-based Sampling Collection Performance Snapshot Algorithm Group Microarchitecture Analysis Group Parallelism Analysis Group Input and Output Analysis Accelerators Analysis Group Platform Analysis Group Platform Analysis Hybrid CPU Analysis Source Code Analysis Custom Analysis Energy Analysis Code Profiling Scenarios Control Data Collection Manage Data Views Manage Result Files
Hardware Event-based Sampling Collection x
Allow Multiple Runs or Multiplex Events Hardware Event-based Sampling Collection with Stacks
Algorithm Group x
Hotspots Analysis for CPU Usage Issues Anomaly Detection Analysis (preview) Memory Consumption Analysis
Hotspots Analysis for CPU Usage Issues x
Hotspots View
Anomaly Detection Analysis (preview) x
Anomaly Detection View
Memory Consumption Analysis x
Memory Consumption and Allocations View
Microarchitecture Analysis Group x
Microarchitecture Exploration Analysis for Hardware Issues Memory Access Analysis for Cache Misses and High Bandwidth Issues
Microarchitecture Exploration Analysis for Hardware Issues x
Microarchitecture Exploration View Microarchitecture Pipe
Memory Access Analysis for Cache Misses and High Bandwidth Issues x
Memory Usage View
Parallelism Analysis Group x
Threading Analysis HPC Performance Characterization Analysis
Threading Analysis x
Threading Efficiency View
HPC Performance Characterization Analysis x
HPC Performance Characterization View
Input and Output Analysis x
Analyze Platform Performance Analyze DPDK Applications Analyze SPDK Applications Analyze Linux Kernel I/O
Accelerators Analysis Group x
GPU Offload Analysis GPU Compute/Media Hotspots Analysis (Preview) CPU/FPGA Interaction Analysis NPU Exploration Analysis (Preview)
GPU Compute/Media Hotspots Analysis (Preview) x
GPU Compute/Media Hotspots View
CPU/FPGA Interaction Analysis x
CPU/FPGA Interaction View
NPU Exploration Analysis (Preview) x
NPU Exploration View
Platform Analysis Group x
System Overview Analysis
System Overview Analysis x
Analyze Interrupts Analyze Latency Issues
Custom Analysis x
Custom Analysis Options
Custom Analysis Options x
Highly Accurate CPU Time Data Collection Hardware Event List Linux* and Android* Kernel Analysis Sampling Interval Sample After Value
Hardware Event List x
Hardware Event Skid Instructions Retired Event Precise Events
Energy Analysis x
Run Energy Analysis View Energy Analysis Data with Intel® VTune™ Profiler Interpret Energy Analysis Data with Intel® VTune™ Profiler
Code Profiling Scenarios x
Java* Code Analysis Python* Code Analysis Configure Python Data Collection Identifying Hot Spots Python Code Profiling Considerations See Also Intel® Threading Building Blocks Code Analysis MPI Code Analysis OpenSHMEM* Code Analysis with Fabric Profiler GPU Application Analysis on Intel® HD Graphics and Intel® Iris® Graphics Frame Data Analysis Task Analysis
GPU Application Analysis on Intel® HD Graphics and Intel® Iris® Graphics x
GPU OpenCL™ Application Analysis Intel® Media SDK Program Analysis
Control Data Collection x
Finalization Pause Data Collection Limit Data Collection Generate Command Line Configuration from GUI Minimize Collection Overhead Import External Data
Import External Data x
Use a Custom Collector Create a CSV File with External Data Import Linux Perf* Trace with VTune Profiler Metrics Examples of CSV Format and Imported Data
Manage Data Views x
Switch Viewpoints Control Window Synchronization View Stacks Manage Grid Views Manage Timeline View Change Threshold Values Choose Data Format Group and Filter Data View Data on Inline Functions Analyze Loops Stitch Stacks for Intel® oneAPI Threading Building Blocks or OpenMP* Analysis Search for Data
View Stacks x
Call Stack Mode Metrics Distribution Over Call Stacks
Manage Result Files x
VTune Profiler Filenames and Locations Import Results and Traces into VTune Profiler GUI Compare Results
Compare Results x
Compare Source Code View Comparison Data
View Comparison Data x
Comparison Summary Bottom-up Comparison Top-down Tree Comparison
Intel® VTune™ Profiler Command Line Interface x
vtune Command Syntax vtune Actions Run Command Line Analysis Work with Results from Command Line Generate Command Line Reports Command Line Usage Scenarios Command Line Interface Reference Report Problems from Command Line
Run Command Line Analysis x
performance-snapshot Command Line Analysis hotspots Command Line Analysis anomaly-detection Command Line Analysis threading Command Line Analysis memory-consumption Command Line Analysis hpc-performance Command Line Analysis uarch-exploration Command Line Analysis memory-access Command Line Analysis tsx-exploration Command Line Analysis tsx-hotspots Command Line Analysis sgx-hotspots Command Line Analysis gpu-hotspots Command Line Analysis gpu-offload Command Line Analysis npu graphics-rendering Command Line Analysis fpga-interaction Command Line Analysis io Command Line Analysis system-overview Command Line Analysis runsa/runss Custom Command Line Analysis Configure Analysis Options from Command Line
Configure Analysis Options from Command Line x
Collect System-Wide Data from Command Line Collect Data on Remote Linux* Systems from Command Line Configure GPU Analysis from Command Line Specify Search Directories from Command Line Specify Result Directory from Command Line Pause Collection from Command Line Manage Analysis Duration from Command Line Limit Data Collection from Command Line
Work with Results from Command Line x
View Command Line Results in the GUI Import Results from Command Line Re-finalize Results from Command Line
Generate Command Line Reports x
Summary Report Hotspots Report Hardware Events Report Callstacks Report Timeline Report Top-down Report GPU Compute/Media Hotspots Report gprof-cc Report Difference Report View Source Objects from Command Line Save and Format Command Line Reports Filter and Group Command Line Reports
Command Line Usage Scenarios x
Use VTune Profiler Server in Containers Android* Target Analysis from the Command Line OpenMP* Analysis from the Command Line Java* Code Analysis from the Command Line
Command Line Interface Reference x
Option Descriptions and General Rules allow-multiple-runs analyze-kvm-guest analyze-system app-working-dir archive call-stack-mode collect collect-with column command cpu-mask csv-delimiter cumulative-threshold-percent custom-collector data-limit discard-raw-data duration filter finalization-mode finalize format group-by help import inline-mode knob kvm-guest-kallsyms kvm-guest-modules limit loop-mode mrte-mode no-follow-child no-summary no-unplugged-mode quiet report report-knob report-output report-width result-dir resume-after return-app-exitcode ring-buffer search-dir show-as sort-asc sort-desc source-object source-search-dir stack-size start-paused strategy target-install-dir target-system target-tmp-dir target-duration-type target-pid target-process time-filter trace-mpi user-data-dir verbose version
API Support x
Instrumentation and Tracing Technology APIs JIT Profiling API System APIs Supported by Intel® VTune™ Profiler
Instrumentation and Tracing Technology APIs x
Basic Usage and Configuration Instrumentation and Tracing Technology API Reference
Basic Usage and Configuration x
Configure Your Build System Attach ITT APIs to a Launched Application Instrument Your Application Minimize ITT API Overhead View Instrumentation and Tracing Technology (ITT) API Task Data in Intel® VTune™ Profiler
Instrumentation and Tracing Technology API Reference x
Domain API String Handle API Collection Control API Thread Naming API Task API Frame API Histogram API User-Defined Synchronization API Event API Counter API Context Metadata API Load Module API Memory Allocation APIs
JIT Profiling API x
Using JIT Profiling API JIT Profiling API Reference
JIT Profiling API Reference x
iJIT_NotifyEvent iJIT_IsProfilingActive iJIT_ GetNewMethodID
Troubleshooting x
Best Practices: Resolve Intel® VTune™ Profiler BSODs, Crashes, and Hangs in Windows* OS Error Message: Application Sets Its Own Handler for Signal Error Message: Cannot Enable Event-Based Sampling Collection Error Message: Cannot Collect GPU Hardware Metrics Error Message: Cannot Load Data File Error Message: Cannot Locate Debugging Information Error Message: Cannot Open Data Error Message: Client Is Not Authorized to Connect to Server Error Message: Root Privileges Required for Processor Graphics Events Error Message: No Pre-built Driver Exists for This System Error Message: Not All OpenCL™ API Profiling Callbacks Are Received Error Message: Problem Accessing the Sampling Driver Error Message: Required Key Not Available Error Message: Scope of ptrace System Call Is Limited Error Message: Stack Size Is Too Small Error Message: Symbol File Is Not Found Problem: Analysis of the .NET* Application Fails Problem: Cannot Access VTune Profiler Documentation Problem: CPU time for Hotspots or Threading Analysis is Too Low Problem: 'Events= Sample After Value (SAV) * Samples' Is Not True If Multiple Runs Are Disabled Problem: Guessed Stack Frames Problem: GUI Hangs or Crashes Problem: Inaccurate Sum in the Grid Problem: Information Collected via ITT API Is Not Available When Attaching to a Process Problem: No GPU Utilization Data Is Collected Problem: Same Functions Are Compared As Different Instances Problem: Skipped Stack Frames Problem: Stack in the Top-Down Tree Window Is Incorrect Problem: Stacks in Call Stack and Bottom-Up Panes Are Different Problem: System Functions Appear in the User Functions Only Mode Problem: VTune Profiler is Slow to Respond When Collecting or Displaying Data Problem: VTune Profiler is Slow on X-Servers with SSH Connection Problem: Unexpected Paused Time Problem: {Unknown Timer} in the Platform Power Analysis Viewpoint Problem: Unknown Critical Error Due to Disabled Loopback Interface Problem: Unknown Frames Problem: Unsupported Microsoft* Windows* OS Warnings about Accurate CPU Time Collection
Reference x
User Interface Reference CPU Metrics Reference GPU Metrics Reference OpenCL™ Kernel Analysis Metrics Reference Energy Analysis Metrics Reference Intel Processor Events Reference
User Interface Reference x
Context Menu: Grid Context Menus: Call Stack Pane Context Menus: Project Navigator Context Menus: Source/Assembly Window Dialog Box: Binary/Symbol Search Dialog Box: Source Search Hot Keys Menu: Customize Grouping Menu: Intel VTune Profiler Pane: Call Stack Pane: Options - General Pane: Options - Result Location Pane: Options - Source/Assembly Project Navigator Pane: Timeline Toolbar: Configure Analysis Toolbar: Filter Toolbar: Source/Assembly Toolbar: Intel VTune Profiler Window: Bandwidth - Platform Power Analysis Window: Bottom-up Window: Caller/Callee Window: Cannot Find <file type> File Window: Collection Log Window: Compare Results Window: Configure Analysis Window: Core Wake-ups - Platform Power Analysis Window: Correlate Metrics - Platform Power Analysis Window: CPU C/P States - Platform Power Analysis Window: Debug Window: Event Count - Hardware Events Window: Flame Graph Window: Graphics - GPU Compute/Media Hotspots Window: Graphics C/P States - Platform Power Analysis Window: NC Device States - Platform Power Analysis Window: Platform Window: Platform Power Analysis Window: Sample Count - Hardware Events Window: SC Device States - Platform Power Analysis Window: Summary Window: System Sleep States - Platform Power Analysis Window: Temperature/Thermal Sample - Platform Power Analysis Window: Timer Resolution - Platform Power Analysis Window: Top-down Tree Window: Uncore Event Count - Hardware Events Window: Wakelocks - Platform Power Analysis
Window: Summary x
Window: Summary - Input and Output Summary Window: Summary - Microarchitecture Exploration Window: Summary - GPU Analysis Window: Summary - Hardware Events Window: Summary - Hotspots by CPU Utilization Window: Summary - HPC Performance Characterization Window: Summary - Memory Consumption Window: Summary - Memory Usage Window: Summary - Platform Power Analysis
GPU Metrics Reference x
ALU0 Active ALU0 Instructions ALU1 Active ALU1 Instructions ALU2 Active ALU2 Instructions ALU0 and ALU1 Active ALU0 and ALU2 Active Average Time Computing Threads Started Computing Threads Started, Threads/sec CPU Time EU 2 FPU Pipelines Active EU Array Active EU Array Idle EU Array Stalled/Idle EU Array Stalled EU IPC Rate EU Send pipeline active EU Threads Occupancy Host to GPU Memory Read Bandwidth Host-to-GPU Memory Write Bandwidth Global GPU EU Array Usage GPU L3 Bound GPU L3 Miss Ratio GPU L3 Misses GPU L3 Misses, Misses/sec GPU Memory Read Bandwidth, GB/sec GPU Memory Texture Read Bandwidth, GB/sec GPU Memory Write Bandwidth, GB/sec GPU Texel Quads Count, Count/sec GPU Utilization Instance Count L3 Read Bandwidth L3 Write Bandwidth L3 Sampler Bandwidth, GB/sec L3 Shader Bandwidth, GB/sec LLC Miss Rate due GPU Lookups LLC Miss Ratio due GPU Lookups Local Maximum GPU Utilization Occupancy PS EU Active % PS EU Stall % Ratio to Max Bandwidth, % Ratio to Max Bandwidth, % Ratio to Max Bandwidth, % Render/GPGPU Command Streamer Loaded Samples Blended Samples Killed in PS, pixels Samples Written Sampler Busy Sampler Is Bottleneck Shared Local Memory Read Bandwidth, GB/sec Shared Local Memory Write Bandwidth, GB/sec SIMD Width Stack-to-stack Incoming Bandwidth Stack-to-stack Outgoing Bandwidth System Memory Read Bandwidth System Memory Write Bandwidth Size Total, GB/sec Total Time Typed Memory Read Bandwidth, GB/sec Typed Memory Write Bandwidth, GB/sec Typed Reads Coalescence Typed Writes Coalescence Untyped Memory Read Bandwidth, GB/sec Untyped Memory Write Bandwidth, GB/sec Untyped Reads Coalescence Untyped Writes Coalescence VS EU Active VS EU Stall
OpenCL™ Kernel Analysis Metrics Reference x
Computing Task Total Time Instance Count SIMD Width SIMD Utilization Work Size
Energy Analysis Metrics Reference x
Available Core Time C-State D0ix States DRAM Self Refresh Energy Consumed (mJ) Idle Wake-ups P-State S0ix States Temperature Timer Resolution Total Time in C0 State Total Time in Non-C0 States Total Time in S0 State Total Wake-up Count Wake-ups Wake-ups/sec per Core
User Guide

Python* Code Analysis

Explore performance analysis options provided by the Intel® VTune™ Profiler for Python* applications to identify the most time-consuming code sections and critical call paths.

VTune Profiler supports the Hotspots, Threading, and Memory Consumption (Linux only) analyses for Python* applications through the Launch Application mode. For example, when your application does excessive numerical modeling, you need to know how effectively it uses available CPU resources. A good example of the effective CPU usage is when the calculating process spends most time executing native extension and not interpreting Python glue code.

To get the maximum performance out of your Python application, consider using native extensions, such as NumPy or writing and compiling performance critical modules of your Python project in native languages, such as C or even assembly. This will help your application take advantage of vectorization and make complete use of powerful CPU resources.

To analyze the Python code performance with the VTune Profiler and interpret data:

Configure Python Data Collection

Configure VTune Profiler through the GUI or command-line (vtune) interface to analyze the performance of your Python code.

In the GUI:

  1. Click the Configure Analysis button on the toolbar.

    The Configure Analysis window opens.

  2. Choose a target system and target type, like Local Host and Launch Application.

    NOTE:

    You can profile Windows* and Linux* target systems only.

  3. In the WHERE pane, provide these details:

    • In the Application field, enter the path to the installed Python interpreter.
    • In the Application parameters field, enter the path to your Python script.
      NOTE:

      If you specify a relative path to your Python script, VTune Profiler completely resolves the full function or method names for the imported modules only. The names inside the main script are not resolved. To avoid this, specify the absolute path to your Python script.

    • In the Advanced settings, in the Managed code profiling mode drop-down menu, select Auto. This way, VTune Profiler automatically detects the type of target executable (managed or native) and switches to the corresponding mode.
    • If necessary, select Analyze child processes to collect data on processes launched by the target process.

    NOTE:

    When you attach the VTune Profiler to the Python process, make sure you initialize the Global Interpreter Lock (GIL) inside your script before you start the analysis. If GIL is not initialized, the VTune Profiler collector initializes it only when a new Python function is called.

  4. If your Python application should run before you start profiling or if you cannot run the application at the start of the analysis, attach VTune Profiler to the Python process. To do this, in the WHAT pane, select the Attach to Process target type. Specify the Python process name or PID.

  5. In the HOW pane on the right, select the Hotspots, Threading, or Memory Consumption analysis type.

  6. If necessary, configure these options or use their default settings:

    User-Mode Sampling mode

    Select to enable the user-mode sampling and tracing collection for hotspots and call stack analysis (formerly known as Basic Hotspots). This collection mode uses a fixed sampling interval of 10ms. If you need to change the interval, click the Copy button and create a custom analysis configuration.

    Show additional performance insights check box

    Get additional performance insights, such as vectorization, and learn next steps. This option collects additional CPU events, which may enable the multiplexing mode.

    The option is enabled by default.

    Details button

    Expand/collapse a section listing the default non-editable settings used for this analysis type. If you want to modify or enable additional settings for the analysis, you need to create a custom configuration by copying an existing predefined configuration. VTune Profiler creates an editable copy of this analysis type configuration.

  7. Click Start to run the analysis.

Identifying Hot Spots

Hotspots analysis in the user-mode sampling mode helps identify sections of your Python code that take a long time to execute (hotspots), along with their timing metrics and call stacks. It also displays the workload distribution over threads in the Timeline pane.

By default, the VTune Profiler uses the Auto managed code profiling mode, that enables you to view and analyze mixed stacks for Python/C++ applications. In the example below, you can see a native hotspot Intel® oneAPI Math Kernel Library(oneMKL) function on the left pane. The mixed call stack analysis on the right pane reveals a Python black_scholes function that actually calls the hotspot function:

Double-click the black_scholes function on the Call Stack pane to open the source view on call site line 66:

To view call stacks only inside your Python code, filter out Python core and system functions by selecting Only user functions option for the Call Stack Mode on the filter bar.

Python Code Profiling Considerations

  • Profiling support exists for Python distribution 2.6 and newer versions.

  • If you use Python extensions that compile Python code to the native language (JIT, C/C++), VTune Profiler may show incorrect analysis results. Consider using JIT Profiling API to solve this problem.

  • You can profile Python code on Windows and Linux target systems.

  • In some cases, VTune Profiler may not resolve full names of Python functions and modules on Windows OS. However, the source information displays properly. You can view the source directly from viewpoints in VTune Profiler.

  • The Timeline pane does not always display proper thread names.

  • If your application has very low stack depth, which includes called functions and imported modules, the VTune Profiler does not collect Python data. Consider using deeper calls to enable the profiling.

  • When collecting data remotely, VTune Profiler may not resolve full function or method names, and display the source code of your Python script. To solve this problem for Linux targets, copy the source files to a directory on your host system with a path identical to the path on your target system before running the analysis.

Parent topic: Code Profiling Scenarios
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