Using Time Profiler in Instruments

Written by Yi

Description: Learn how to make your apps faster and more efficient in this introduction to Time Profiler in Instruments. Walk through how to use Time Profiler to measure your app's performance. Learn how Time Profiler works and can be used to identify problems and verify your fixes. Discover how easy it is to improve your app's power usage and performance by using Instruments throughout your development process.

Intro to profiling

How much and what kind of work is my app doing?

The Call Tree displays counter of each calls, it doesn't measure the duration of each calls, even ignore/lose some quick/short calls. So, it shows long running calls and the most repetitive calls.

Going faster

  • Focused on an area of high CPU usage
    • Zoom and Select area will help focus on the "Problems", which filters out all other unrelevant calls.
    • Examined the call tree, looking for where the work was happening
    • Walked back to our code
    • Inspected our code
    • Made it faster
    • Verified the changes
    • Saved the user's time

Doing less

  • Focused on low, but unexpected CPU usage
  • Examined the call tree
  • Determined the frameworks involved
  • Stopped doing unnecessary work
  • Verified the changes
  • Improved battery life

Improving responsiveness

Problem: The main thread does all the UI work

  • Runloop waiting for events
  • Sends events to your UIApplication instance
  • Passes through the responder chain
  • Your code gets invoked

Phenomenon: When busy, the main thread can't process events

  • The queue backs up
  • Stuttering and hiccups
  • App becomes unresponsive

What to Do: Keep your main thread Free

  • Examined the CPU spikes
  • Focused on the main thread
  • Identified non-UI work happening on the main thread
  • Distributed the work across multiple threads
  • Verified the changes
  • Achieved a better user experience

Tips

  • Always profile release builds
  • Always profile on the device
  • Run with old devices
  • Use large data sets where it makes sense
  • Look for poorly scaling code (O(n^2), etc.)

Summary

  • Optimize High CPU usage
  • Remove Unexpected CPU usage
  • Move heavy work away from main thread

This note was originally published at github.com/antonio081014/WWDC_Learning_Review.

Missing anything? Corrections? Contributions are welcome 😃

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Yi

Reveal a brilliant idea in a very mobile engineer way.