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In the development journey of HarmonyOS Next, parallel concurrent programming is like a tense and exciting multi-threaded dance. Developers strive to make each thread coordinate and cooperate to make beautiful dance steps.However, the actual situation is often like a chaotic group dance, with problems such as "pseudo-parallel" and parallel failure occurring frequently.But don't worry, the visual parallel concurrent tuning tool is like a magical dance instructor that will bring us a whole new solution.
Requirements background for parallel concurrent tuning
In traditional Android and iOS development, parallel concurrent programming is already a headache.In the distributed environment of HarmonyOS Next, this problem becomes more complicated.Imagine you are developing a distributed smart home system, with multiple devices running at the same time, each device having its own task thread.If these threads cannot be executed effectively in parallel, it will lead to slow system response and greatly reduced user experience.
It's like a football game, where every player is a thread, and they need to collaborate in parallel on the court to complete offensive and defensive tasks together.If the players do not cooperate well, problems such as passing mistakes and overlapping running positions will occur, which will affect the results of the game.In HarmonyOS Next development, "pseudo-parallel" is like players seemingly running, but in fact it does not really form an effective offensive cooperation; parallel failure is like players interfering with each other and unable to exert their respective abilities.
For example, in a multi-threaded file handler, it was originally desirable for multiple threads to process different file blocks at the same time to improve processing speed.However, due to thread synchronization problems, a thread may be waiting for other threads to release resources, resulting in the overall performance not being improved. This is a typical manifestation of "pseudo-parallelism".
Visual Tuning Tool Function Preview
The visual parallel concurrent tuning tool that will be launched in the future is like equipping developers with a pair of perspective glasses, which allows us to clearly see the internal operation of parallel concurrent programs.
Task statistics display of different concurrency modes
This tool can visually display Task statistics in different concurrency modes.Just like a football coach uses data analysis software to understand how each player performs under different tactics.Developers can see information such as execution time, waiting time, CPU occupancy, etc. of each Task.Through this information, we can analyze which Tasks are the bottlenecks and which concurrent modes are more efficient.
Check the operation status of a single task
In addition to overall statistics, the tool also allows us to see the operation of a single task in depth.This is like a coach can carefully observe each player's specific movements during the game through video playback.Developers can see the Task execution process, call stack information, and state changes at different points in time.In this way, we can accurately find out the problems in the Task execution process, such as deadlocks, resource competition, etc.
Examples are shown in combination with the figure
Imagine that tools display this information in the form of a chart.The horizontal coordinate represents time, and the vertical coordinate represents CPU occupancy or the status of the task.Different colors of lines represent different tasks.By observing these charts, we can intuitively see the collaboration between each task.For example, if a line is always in a waiting state, it means that the task may have encountered problems and needs further investigation.
// The following is a simple multithreaded sample code
import threading
def task_function():
# Simulate task processing
for i in range(1000000):
pass
threads = []
for _ in range(5):
thread = threading.Thread(target=task_function)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
In this example, we create 5 threads to perform the same task.Using the visual tuning tool, we can observe the execution of these 5 threads and analyze whether there is a problem of inefficiency in parallel.
The positive impact of tuning tools on development
The emergence of this visual tuning tool will have a huge positive impact on HarmonyOS Next development.
Improve development efficiency
Developers can quickly locate problems in parallel concurrent programs through tools without spending a lot of time manually debugging and analyzing.Just like a football coach quickly discovers players' problems through data analysis software and adjusts tactics in a timely manner.Developers can optimize code in a targeted manner and improve development efficiency based on the information provided by the tool.
Optimize application performance
By tuning parallel concurrent programs, the performance of the application will be significantly improved.In a distributed environment, collaboration between various devices will be more efficient, the system will respond faster, and the user experience will be better.This is like in football games, the cooperation between players is more tacit, the offense and defense are smoother, and the results of the game are naturally better.
Lower development threshold
For beginners, parallel concurrent programming has always been a difficult threshold to overcome.The emergence of visual tuning tools will make parallel concurrent programming more intuitive and easy to understand.Beginners can gradually master the skills of parallel concurrent programming by observing the information displayed by the tool, which lowers the development threshold.
In short, the visual parallel concurrency tuning tool paints a bright future for HarmonyOS Next development.It will help developers better deal with the challenges of parallel concurrent programming, improve application performance and development efficiency.Let us look forward to the official launch of this tool and contribute to the development of HarmonyOS Next!