• 2023
  • 12/16/2022
  • Public Content

Inefficient Synchronization

This recipe shows how to locate inefficient synchronization in your code by running the Advanced Hotspots analysis of the Intel® VTune™ Amplifier with the stack collection enabled.
Advanced Hotspots analysis was integrated into the generic Hotspots analysis starting with Intel VTune Amplifier 2019, and is available via the Hardware Event-Based Sampling collection mode.


This section lists the hardware and software tools used for the performance analysis scenario.
  • Application:
    using OpenMP* runtime. The application is used as a demo and not available for download.
  • Performance analysis tools:
    Intel VTune Amplifier 2017: Advanced Hotspots analysis
    • Starting with the 2020 release, Intel® VTune™ Amplifier has been renamed to
      Intel® VTune™
    • Most recipes in the
      Intel® VTune™
      Performance Analysis Cookbook are flexible. You can apply them to different versions of
      Intel® VTune™
      . In some cases, minor adjustments may be required.
    • Get the latest version of
      Intel® VTune™
  • Operating system:
    Microsoft* Windows* 8
  • CPU:
    Intel® microarchitecture code named Skylake

Run the Advanced Hotspots Analysis with Stacks

Launch the VTune Amplifier and configure your project for analysis:
  1. Click the
    New Project
    button on the toolbar and specify a name for the new project, for example:
  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
    Algorithm Analysis > Advanced Hotspots
    and select the
    Hotspots and stacks
  5. Click
    VTune Amplifier launches the application, collects data, finalizes the data collection result resolving symbol information, which is required for successful source analysis.

Locate Synchronization on the Timeline

Open the data collected during the analysis in the Hardware Events viewpoint:
Select the
User/system functions
call stack mode to display both user and system functions in the
Call Stack
In the
Call Stack
pane, select the
Synchronization Context Switch Count
type from the drop-down menu to see a call stack for the synchronization context switch selected in the
Locate a frequent synchronization on the timeline and hover over a context switch to view details in the tooltip. For example, in the Advanced Hotspots result above, the
thread has the largest number of context switches caused by synchronization. When you select a context switch on the timeline, the
Call Stack
pane is updated to show a call sequence at which a preceding thread execution quantum was interrupted.

Analyze an Average Wait Metric

Click the
link to open the Hotspots viewpoint:
Analyze the Wait Rate metric data, that is average wait time (in milliseconds) per synchronization context switch. This metric helps you identify ineffective frequent synchronizations as well as power consumption issues.
interprets low Wait Rate metric values (under 1ms) as performance issues and highlights them in pink. These values may signal an increased contention between threads and inefficient use of system API.
Identify a synchronization stack with short Wait time and high CPU time (half the time of all system calls) and double-click it to explore the source code of the hotspot function.

Analyze Synchronization Context Switches

Click the
link to open to the Hardware Events viewpoint. By default, the
Event Count
grid is sorted by the Clockticks event. Identify the hottest functions that took the most CPU time (in clockticks) to execute and had the most frequent synchronization.
In this sample OpenMP* application, the VTune Amplifier identifies the
function as a primary computation hotspot called from an OpenMP region. You can also see a major contention on the
inside the OpenMP runtime that results in ~ 30% of performance loss (Clockticks of the wait function / Clockticks of the hotspot function).
Double-click the
function to view the source code and identify a cause of ineffective synchronization.
for(i = 0; i < block_no; i++) { #pragma omp parallel for for(j = 0; j < lines_in_block; j++) { /// do processing } /// implicit barrier causing contention and overhead }
Code analysis for the sample application discovers excessive OpenMP barriers added to process a picture by blocks of lines and parallelize each block separately. To resolve this issue, use the
clause or apply
to the entire picture and use dynamic work scheduling.
For the optimized result, the relative cost of contention on
is low (26,997).
Using a single
and dynamic work scheduling for the
function helped decrease the contention and negative performance impact down to ~1%.
The second optimized result also discovers another highly contended function
(Synchronization Context Switches metric equal to 26,997). But if you check its execution time, it is within 2% of the top hotspot (not shown), which makes it less important. But this function may become an issue when running the application on a greater number of processors.
The initial (pre-optimized) sample data collection session represented above was taken on a limited time interval. The optimized version represents a full application run.

Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at