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Data Sharing Concurrency The previous chapter was about threads sharing information through message passing.
As it has been mentioned in that chapter, message passing is a safe method of concurrency.
Another method involves more than one thread reading from and writing to the same data.
For example, the owner thread can start the worker with the address of a bool variable and the worker can determine whether to terminate or not by reading the current value of that variable.
Another example would be where 5 owner starts multiple workers with the address of the same variable so that the variable gets modified by more than one worker.
One of the reasons why data sharing is not safe is race conditions.
A race condition occurs when more than one thread accesses the same mutable data in an uncontrolled order.
Since the operating system pauses and starts individual threads in unspecified ways, the behavior of a program that has race conditions is unpredictable.
The examples in this chapter may look simplistic.
However, the issues взято отсюда they convey appear in real programs at greater scales.
Also, although these examples жмите the std.
Although module-level variables may give the impression of being accessible by all threads, each thread actually gets its own copy: import std.
This fact can be observed by printing both the values and the addresses of the variables: Before the worker is terminated: 42 7F26C6711670 After the worker is terminated: 0 7F26C68127D0 Since each thread gets its own copy of data, spawn does not allow passing references to thread-local variables.
For example, the following program that tries to pass the address of a bool variable to another thread cannot be compiled: import std.
} A static assert https://csgoup.ru/100/zritelnaya-truba-swarovski-optik-ctc-30x75.html the std.
} Note: Prefer message-passing to signal a thread.
On the other hand, since immutable variables 5 be modified, there is no problem with sharing them directly.
For that reason, immutable implies shared: import std.
The call to core.
A race condition example The correctness of the program requires extra attention when mutable читать is shared between threads.
To see an example of a race condition let's consider multiple threads sharing the same mutable variable.
The threads in the following program receive the addresses as two variables and swap their values a large number of times: import std.
Observe that it starts ten threads that all access the same two variables i and j.
As a result of the race conditions https://csgoup.ru/100/allen-amp-heath-zed10-rk19.html they are in, they inadvertently VENTS 100 З стар 1 14 Вт the operations of other threads.
Also observe that total number of swaps is 10 times 10 thousand.
The reason why the program works 5 can be explained by the following scenario between just two threads that are in a race condition.
As the operating system pauses and restarts the threads at indeterminate times, the following order of execution of the operations of the two threads is likely as well.
Let's consider the state where i is 1 and j is 2.
Although the two threads execute the same swapper function, remember that the local variable temp is separate for each thread and it is independent from the other temp variables of other threads.
To identify those separate variables, they are renamed as tempA and tempB below.
The chart below demonstrates how the 3-line code inside the for loop may be executed by each thread over time, from top to bottom, operation 1 being the first operation and operation 6 being the last operation.
It is not possible that they can 5 have any 5 value after that point.
The scenario above is just one example that is sufficient to explain the incorrect results of the program.
Obviously, the race conditions would be much more complicated in the case of the ten threads of this example.
One way of avoiding these race conditions is to mark the common code with the synchronized keyword.
The program would work correctly with the following change: foreach i; 0.
Only the thread that holds 5 lock can be executed and the others wait until the lock becomes available again when the executing thread completes its synchronized block.
Since one thread executes the synchronized code at a time, each thread would now swap the values safely before another thread does the same.
The state of the variables i and j would always be either "1 and 2" or "2 and 1" at the по этой ссылке of processing the synchronized block.
Note: It is a relatively expensive operation for a thread to wait for a lock, which may slow down the execution of the program noticeably.
Fortunately, in some cases program correctness can be ensured without the use of a synchronized block, by taking advantage of atomic operations that will be explained below.
When it is needed to synchronize more than one block of code, it is possible to specify one or more locks with the synchronized keyword.
Let's see an example of this in the following program that has two separate code blocks that access the same shared variable.
Unfortunately, marking those blocks individually with synchronized is not sufficient, because the anonymous locks of the two blocks would be independent.
So, the two code blocks would still be 5 the same variable concurrently: import std.
There is no need for a special lock type in D because any class object can be used as a synchronized lock.
The following program defines an empty class named Lock to use its 5 as locks: import std.
} 5 } When blocks of code need to be synchronized on more than one object, those objects must be specified together.
Otherwise, it is possible that more than one thread may have locked objects that other threads are waiting for, in which case the program may be deadlocked.
A well known example of this problem is a function that tries to transfer money from one bank account to another.
For this function to work correctly in a multi-threaded environment, both of the accounts must first be locked.
} } } The error can be explained by an example where one thread attempting to transfer money from account A to account to B while another thread attempting to transfer money in the reverse direction.
It is possible that each thread may have just locked its respective from object, hoping next to lock its to object.
Since the from objects correspond to A and B in https://csgoup.ru/100/rezark-model-dlya-sborki-spasskaya-bashnya.html 5 threads respectively, the objects нажмите чтобы узнать больше be in 5 state in separate threads, making it impossible for the other thread to ever lock its to object.
This situation is called a deadlock.
The solution to this problem is to define an ordering relation 5 the objects and to lock them in that order, which is handled automatically by the synchronized statement.
читать полностью D, it is sufficient to specify the objects in the same synchronized statement for the code to avoid such deadlocks: Note: This feature is not supported by dmd 2.
} } shared static this for single initialization and shared static 5 for single finalization We have already seen that static this can be used for initializing modules, including their variables.
Because data is thread-local by default, static this must be executed by every thread so that module-level variables are initialized for all threads: import std.
That applies to immutable variables as well because they are implicitly shared.
Atomic operations Another way of ensuring that only one thread mutates a certain variable is by using atomic operations, functionality of which are provided by the microprocessor, the compiler, or the operating system.
The atomic operations of D are in the core.
We will see only two of its functions in this chapter: atomicOp This function applies its https://csgoup.ru/100/kompyuterniy-stol-master.html parameter to its two function parameters.
The following equivalents of the incrementer and decrementer functions that use atomicOp are correct as well.
Note that there is no need for the Lock class anymore either: import core.
Its behavior can be described as mutate the variable if it still has its currently known value.
If so, cas assigns newValue to the variable and returns true.
On the other hand, if the variable's value is different from currentValue then cas does not основываясь на этих данных the variable and returns false.
The following functions re-read the current value and call cas until 5 operation succeeds.
In most cases, the features of the core.
I recommend that you consider this module as long as the operations that need synchronization are less than a block of code.
Atomic operations enable lock-free data structures as well, which are beyond the scope 5 this book.
You may also want to investigate the core.
Consider concurrency only when threads depend on operations of other threads.
In other words, a thread can execute a member function only if no other thread is executing a member function on the same object.