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HomeBlogProgrammingWhat Is Multithreading in Python
Basically, a thread is an independent flow of execution. Multithreading allows the execution of multiple parts of a program at the same time. For example, if you are playing a game on your PC, the whole game is one process but it contains several threads, which are used by the user, which are used synchronously to run the opponent, etc.
These are all separate threads responsible for carrying out these different tasks in the same program.
Each process has a main thread that’s always running. This main thread creates objects for the child thread. The thread of the child is also started by the main thread.
The software needs to make decisions quickly in many applications. Parallel programming in C/C++ and multiple threading are the best ways to do this.
It is the use of multiple resources, in this case, processors, to solve a problem. The programming of this type takes a problem, splits it into a number of smaller steps, provides instructions and processors to simultaneously execute the solutions.
It is also a form of programming that provides the same programming results, but in less time and more effectively. This programming is used by many computers, such as laptops and private desktops in their hardware to ensure that tasks are done in the background quickly.
Using parallel structures, all processes are speeded up, increasing performance and energy to produce fast results. Parallel computing is easier than concurrent programming simply because the effects are the same in less time. This is extremely important because parallel processes are necessary to collect large data in data sets that can be processed easily or to solve complicated problems.
Parallel programming has several disadvantages. The first is that it can be hard to understand. It takes time for programming that aims at parallel architectures first to be fully understood.
Moreover, code tweaking is not simple and must be modified to improve performance for various target architectures. Consistent results are also hard to estimate because the communication of results for certain architectures may be problematic.
For those establishing many processors for different architectures, the energy consumption is a challenge; a range of cooling technologies are necessary to cool down the parallel cluster.
There’s a broad concept of parallel programming. It is possible to define various types of processes, operating on different machines or on the same machine.
Multithreading refers specifically to the simultaneous execution of more than one series of instructions (thread).
Multithreaded programming consists of several concurrent threads for execution. The threads could be running on one processor, or there might be multiple threads running on multiple processor cores.
Multithreading on a single processor gives the illusion of running in parallel. In reality, a scheduling algorithm is used to switch the processor. Or it switches on how the threads were prioritized by a combination of external inputs (interrupts).
Multithreading is completely parallel on many processor cores. In order to accomplish the result more effectively, individual microprocessors operate together. There are many overlapping, simultaneous activities.
Many threads in a network share the same data space with the main thread and thus can share or interact better than if different processes are involved.
Threads often relate to lightweight processes and don’t need a lot of overhead memory.
The full clock speed of the processors is achieved. Parallelism is the only way to break out of CPUs. Multithreading allows multiple, concurrent threads to be spawned by a single processor.
Each thread executes its own sequence of instructions. They all access the same common memory space and if necessary, communicate with each other. The threads should be optimized carefully for performance.
When we reach the limits of what can be achieved on a single processor, multiple processor cores are used to perform additional tasks. This is critical for AI in particular.
Autonomous driving is an example of this. Humans have to make fast choices in a traditional car, and human reaction time is 0.25 seconds on average.
AI must take these decisions very rapidly within autonomous vehicles — within tenths of a second.
The best way to ensure that these decisions are taken in a necessary time frame is by use of C multi-threading and parallel programming in C.
The switch from single-threaded to multi-threaded programs increases complexity. Programming languages, including C and C++, have been developed to allow the use and management of several threads. Both C and C++ now have libraries for the thread.
Modern C++ has made parallel programming much simpler in particular. A basic threading library was included in C++11. C++17 has added parallel algorithms—and parallel implementations of many standard algorithms.
Multithreading in C has many advantages. But there are also concurrency issues that may arise, and these mistakes will impact the software and lead to safety risks.
Multithreading is very useful but cannot be used everywhere for saving time and better performance. It can also only be used if there is no dependency between threads.
Through the use of multiple threads, you can get more from a single processor. But then these threads have to synchronize their work into a common memory. That can be hard to do — and much harder to do without concurrency issues.
These potential issues are unlikely to be found by conventional testing and debugging methods. You might run a test or a debugger once—and you won’t see any errors. However, there’s a bug when you run it again. You could potentially continue to keep testing and still may not find the issue.
Here are two common forms of multi-threading problems with testing and debugging alone.
When two threads concurrently access a common variable, a race condition occurs. The variable is read in the first thread and the variable reads the same value in the second thread.
Then the first and second threads work on the value, and they aim to see which thread will add the last value to the shared variable. The thread value is retained since the thread is written over the value that the previous thread wrote.
A data race occurs whenever two or more threads access shared data and try to change it simultaneously — without correct synchronization.
This kind of mistake may cause crashes or corruption of memory.
The most common symptom of a race condition is that variables shared by multiple threads are unpredictable values. This is due to the unpredictability of the sequence of threads. Sometimes one thread wins and sometimes the other. Execution functions properly on all occasions. Also, the value of the variable is right if each thread is executed separately.
A deadlock occurs if two threads at the same time lock a different variable and then try to lock the variable already locked by the other thread. Each thread then stops running and waits to release the variable for the other thread.
Since each thread holds the variable that the other thread requires, there is nothing happening, and the threads remain deadlocked. This type of error can cause programs to get stuck.
The programming languages C and C++ have introduced enabling of multi-threading. But there are additional steps you would have to take to ensure stable multithreading without any errors or security problems.
The key to secure multithreading in C/C++ is to use a coding standard. Standards such as CERT allow possible security vulnerabilities to be detected easily. CERT also protects concurrency sections.
With a static analyzer you can apply a secure code standard and perform automated dataflow analysis.
A static analysis tool can detect potential errors, and you will find the bugs you might not have seen before.
The method of identification of possible multi-threading errors is far more accurate. In the earlier development phase, you can use static analyzers if errors are cheaper to fix.
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Conclusion:
Multithreading is very useful, but it cannot be used everywhere to save time and improve efficiency. It can only be used if there is no dependence between threads. Importing the threading module enables multithreading in Python. Parallel computing is easier than concurrent programming simply because you get the same effects in less time.
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