Intel® VTune™ Profiler Performance Analysis Cookbook

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ID 766316
Date 9/05/2023
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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 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 High Bandwidth Memory Performance on Intel® Xeon® CPU Max Series Profiling Windows* Applications for Hybrid CPU Platforms (NEW) 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 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 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 Ingredients Run General Exploration Analysis Identify Hardware Hotspots Re-compile Your Code with PGO Verify Optimization See Also 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

Instruction Cache Misses

This recipe explores profiling a front-end-bound application using the General Exploration analysis of the Intel® VTune™ Amplifier and using a PGO option to reduce ICache misses.

NOTE:

General Exploration analysis was renamed to Microarchitecture Exploration analysis staring with Intel VTune Amplifier 2019.

Ingredients

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

  • Application: a test sample based on sqlite database. The application is used as a demo and not available for download.

  • Tools:

    • Intel VTune Amplifier 2018: General Exploration analysis

      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:

    • Intel® C++ Compiler

  • Operating system: Microsoft* Windows* 7

  • CPU: Intel® microarchitecture code named Skylake

Run General Exploration Analysis

To have a high-level understanding of potential performance bottlenecks for the sample, start with the General Exploration analysis provided by the VTune Amplifier:

  1. Click the New Project button on the toolbar and specify a name for the new project, for example: sqlite.

  2. In the Analysis Target window, select the local host target system type for the host-based analysis.

  3. Select the Launch Application target type and specify an application for analysis on the right.

  4. Click the Choose Analysis button on the right, select Microarchitecture Analysis > General Exploration and click Start.

    VTune Amplifier launches the application, collects data, finalizes the data collection result resolving symbol information, which is required for successful source analysis.

Identify Hardware Hotspots

General Exploration analysis helps you see dominant performance bottlenecks in your code. Start your analysis with the Summary view that displays application-level statistics per hardware metrics. Focus on the flagged performance issues:

The sample application is front-end bound (29.3% of Pipeline Slots) with the instruction cache misses as a dominant bottleneck (7.1% of Clockticks).

Switch to the Bottom-up tab to locate the issue in the code. Click the Customize Grouping button next to the Grouping toolbar and create a new custom grouping Module/Source File:

The new grouping applied to the collected results shows that sqlite3.c file is the main hotspot taking the most of CPU cycles to execute:

Navigating to the ICache Misses metric also discovers the highest value for the sqlite3.c file:

Re-compile Your Code with PGO

Use Intel® C++ Compiler to apply the Profile Guided Optimization (PGO) to the sqlite library:

  1. Recompile your code with the /Qprof-gen option.

  2. Run the benchmark.

  3. Re-compile the code with the /Qprof-use option.

For more details, see the Profile-Guided Optimizations Overview.

Verify Optimization

Re-run the General Exploration analysis on the optimized code. The new result shows 30.3 seconds of Elapsed time, which is an almost 4% improvement to the original 31.5 seconds:

The number of clockticks stalled due to ICache Misses for the sqlite library has reduced to 6.4% from 9.3%:

Parent topic: Tuning Recipes
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