When using monitors for most concurrency problems, you can just put the critical section inside a monitor method and then invoke the method. However, there are some multiplexing problems wherein up to n threads can run their critical sections simultaneously. So we can say that it's useful to know how to use a monitor like the following:
monitor.enter();
runCriticalSection();
monitor.exit();
What can we use inside the monitors so we can go about doing this?
Side question: Are there standard resources tackling this? Most of what I read involve only putting the critical section inside the monitor. For semaphores there is "The Little Book of Semaphores".
As far as I understand your question, any solution must satisfy this:
When fewer than n threads are in the critical section, a thread calling monitor.enter() should not block—i.e. the only thing preventing it from progressing should be the whims of the scheduler.
At most n threads are in the critical section at any point in time; implying that
When thread n+1 calls monitor.enter(), it must block until a thread calls monitor.exit().
As far as I can tell, your requirements are equivalent to this:
The "monitor" is a semaphore with an initial value of n.
monitor.enter() is semaphore.prolaag() (aka P, decrement or wait)
monitor.exit() is semaphore.verhoog() (aka V, increment or signal)
So here it is, a semaphore implemented from a monitor:
monitor Semaphore(n):
int capacity = n
method enter:
while capacity == 0: wait()
capacity -= 1
method exit:
capacity += 1
signal()
Use it like this:
shared state:
monitor = Semaphore(n)
each thread:
monitor.enter()
runCriticalSection()
monitor.exit()
The other path
I guess that you might want some kind of syntactic wrapper, let's call it Multimonitor, so you can write something like this:
Multimonitor(n):
method critical_section_a:
<statements>
method critical_section_b:
<statements>
And your run-time environment would ensure that at most n threads are active inside any of the monitor methods (in your case you just wanted one method). I know of no such feature in any programming language or runtime environment.
Perhaps in python you can create a Multimonitor class containing all the book-keeping variables, then subclass from it and put decorators on all the methods; a metaclass-involving solution might be able to do the decorating for the user.
The third option
If you implement monitors using semaphores, you're often using a semaphore as a mutex around monitor entry and resume points. I think you could initialize such a semaphore with a value larger than one and thereby produce such a Multimonitor, complete with wait() and signal() on condition variables. But: it would do more than what you need in your stated question, and if you use semaphores, why not just use them in the basic and straightforward way?
Related
Coding in Lua, I have a triply nested loop that goes through 6000 iterations. All 6000 iterations are independent and can easily be parallelized. What threads package for Lua compiles out of the box and gets decent parallel speedups on four or more cores?
Here's what I know so far:
luaproc comes from the core Lua team, but the software bundle on luaforge is old, and the mailing list has reports of it segfaulting. Also, it's not obvious to me how to use the scalar message-passing model to get results ultimately into a parent thread.
Lua Lanes makes interesting claims but seems to be a heavyweight, complex solution. Many messages on the mailing list report trouble getting Lua Lanes to build or work for them. I myself have had trouble getting the underlying "Lua rocks" distribution mechanism to work for me.
LuaThread requires explicit locking and requires that communication between threads be mediated by global variables that are protected by locks. I could imagine worse, but I'd be happier with a higher level of abstraction.
Concurrent Lua provides an attractive message-passing model similar to Erlang, but it says that processes do not share memory. It is not clear whether spawn actually works with any Lua function or whether there are restrictions.
Russ Cox proposed an occasional threading model that works only for C threads. Not useful for me.
I will upvote all answers that report on actual experience with these or any other multithreading package, or any answer that provides new information.
For reference, here is the loop I would like to parallelize:
for tid, tests in pairs(tests) do
local results = { }
matrix[tid] = results
for i, test in pairs(tests) do
if test.valid then
results[i] = { }
local results = results[i]
for sid, bin in pairs(binaries) do
local outcome, witness = run_test(test, bin)
results[sid] = { outcome = outcome, witness = witness }
end
end
end
end
The run_test function is passed in as an argument, so a package can be useful to me only if it can run arbitrary functions in parallel. My goal is enough parallelism to get 100% CPU utilization on 6 to 8 cores.
Norman wrote concerning luaproc:
"it's not obvious to me how to use the scalar message-passing model to get results ultimately into a parent thread"
I had the same problem with a use case I was dealing with. I liked lua proc due to its simple and light implementation, but my use case had C code that was calling lua, which was triggering a co-routine that needed to send/receive messages to interact with other luaproc threads.
To achieve my desired functionality I had to add features to luaproc to allow sending and receiving messages from the parent thread or any other thread not running from the luaproc scheduler. Additionally, my changes allow using luaproc send/receive from coroutines created from luaproc.newproc() created lua states.
I added an additional luaproc.addproc() function to the api which is to be called from any lua state running from a context not controlled by the luaproc scheduler in order to set itself up with luaproc for sending/receiving messages.
I am considering posting the source as a new github project or contacting the developers and seeing if they would like to pull my additions. Suggestions as to how I should make it available to others are welcome.
Check the threads library in torch family. It implements a thread pool model: a few true threads (pthread in linux and windows thread in win32) are created first. Each thread has a lua_State object and a blocking job queue that admits jobs added from the main thread.
Lua objects are copied over from main thread to the job thread. However C objects such as Torch tensors or tds data structures can be passed to job threads via pointers -- this is how limited shared memory is achieved.
This is a perfect example of MapReduce
You can use LuaRings to accomplish your parallelization needs.
Concurrent Lua might seem like the way to go, but as I note in my updates below, it doesn't run things in parallel. The approach I tried was to spawn several processes that execute pickled closures received through the message queue.
Update
Concurrent Lua seems to handle first-class functions and closures without a hitch. See the following example program.
require 'concurrent'
local NUM_WORKERS = 4 -- number of worker threads to use
local NUM_WORKITEMS = 100 -- number of work items for processing
-- calls the received function in the local thread context
function worker(pid)
while true do
-- request new work
concurrent.send(pid, { pid = concurrent.self() })
local msg = concurrent.receive()
-- exit when instructed
if msg.exit then return end
-- otherwise, run the provided function
msg.work()
end
end
-- creates workers, produces all the work and performs shutdown
function tasker()
local pid = concurrent.self()
-- create the worker threads
for i = 1, NUM_WORKERS do concurrent.spawn(worker, pid) end
-- provide work to threads as requests are received
for i = 1, NUM_WORKITEMS do
local msg = concurrent.receive()
-- send the work as a closure
concurrent.send(msg.pid, { work = function() print(i) end, pid = pid })
end
-- shutdown the threads as they complete
for i = 1, NUM_WORKERS do
local msg = concurrent.receive()
concurrent.send(msg.pid, { exit = true })
end
end
-- create the task process
local pid = concurrent.spawn(tasker)
-- run the event loop until all threads terminate
concurrent.loop()
Update 2
Scratch all of that stuff above. Something didn't look right when I was testing this. It turns out that Concurrent Lua isn't concurrent at all. The "processes" are implemented with coroutines and all run cooperatively in the same thread context. That's what we get for not reading carefully!
So, at least I eliminated one of the options I guess. :(
I realize that this is not a works-out-of-the-box solution, but, maybe go old-school and play with forks? (Assuming you're on a POSIX system.)
What I would have done:
Right before your loop, put all tests in a queue, accessible between processes. (A file, a Redis LIST or anything else you like most.)
Also before the loop, spawn several forks with lua-posix (same as the number of cores or even more depending on the nature of tests). In parent fork wait until all children will quit.
In each fork in a loop, get a test from the queue, execute it, put results somewhere. (To a file, to a Redis LIST, anywhere else you like.) If there are no more tests in queue, quit.
In the parent fetch and process all test results as you do now.
This assumes that test parameters and results are serializable. But even if they are not, I think that it should be rather easy to cheat around that.
I've now built a parallel application using luaproc. Here are some misconceptions that kept me from adopting it sooner, and how to work around them.
Once the parallel threads are launched, as far as I can tell there is no way for them to communicate back to the parent. This property was the big block for me. Eventually I realized the way forward: when it's done forking threads, the parent stops and waits. The job that would have been done by the parent should instead be done by a child thread, which should be dedicated to that job. Not a great model, but it works.
Communication between parent and children is very limited. The parent can communicate only scalar values: strings, Booleans, and numbers. If the parent wants to communicate more complex values, like tables and functions, it must code them as strings. Such coding can take place inline in the program, or (especially) functions can be parked into the filesystem and loaded into the child using require.
The children inherit nothing of the parent's environment. In particular, they don't inherit package.path or package.cpath. I had to work around this by the way I wrote the code for the children.
The most convenient way to communicate from parent to child is to define the child as a function, and to have the child capture parental information in its free variables, known in Lua parlances as "upvalues." These free variables may not be global variables, and they must be scalars. Still, it's a decent model. Here's an example:
local function spawner(N, workers)
return function()
local luaproc = require 'luaproc'
for i = 1, N do
luaproc.send('source', i)
end
for i = 1, workers do
luaproc.send('source', nil)
end
end
end
This code is used as, e.g.,
assert(luaproc.newproc(spawner(randoms, workers)))
This call is how values randoms and workers are communicated from parent to child.
The assertion is essential here, as if you forget the rules and accidentally capture a table or a local function, luaproc.newproc will fail.
Once I understood these properties, luaproc did indeed work "out of the box", when downloaded from askyrme on github.
ETA: There is an annoying limitation: in some circumstances, calling fread() in one thread can prevent other threads from being scheduled. In particular, if I run the sequence
local file = io.popen(command, 'r')
local result = file:read '*a'
file:close()
return result
the read operation blocks all other threads. I don't know why this is---I assume it is some nonsense going on within glibc. The workaround I used was to call directly to read(2), which required a little glue code, but this works properly with io.popen and file:close().
There's one other limitation worth noting:
Unlike Tony Hoare's original conception of communicating sequential processing, and unlike most mature, serious implementations of synchronous message passing, luaproc does not allow a receiver to block on multiple channels simultaneously. This limitation is serious, and it rules out many of the design patterns that synchronous message-passing is good at, but it's still find for many simple models of parallelism, especially the "parbegin" sort that I needed to solve for my original problem.
so far I thought that any operation done on "shared" object (common for multiple threads) must be protected with "synchronize", no matter what. Apparently, I was wrong - in the code I'm studying recently there are plenty of classes (thread-safe ones, as the Author claims) and only one of them uses Critical Section for almost every method.
How do I find what parts / methods of my code needs to be protected with CriticalSection (or any other method) and which not?
So far I haven't stumbled upon any interesting explanation / article / blog note, all google results are:
a) examples of synchronization between thread and the GUI. From simple progressbar to most complex, but still the lesson is obvious: each time you access / modify the property of GUI component, do that in "Synchronize". But nothing more.
b) articles explaining Critical Sections, Mutexes etc. Just a different approaches of protection/synchronization.
c) Examples of very very simple thread-safe classes (thread safe stack or list) - they all do the same - implement lock / unlock methods which do enter/leave critical section and return the actual stack/list pointer on locking.
Now I'm looking for explanation which parts of code should be protected.
could be in form of code ;) but please don't provide me with one more "using Synchronize to update progressbar" ... ;)
thank you!
You are asking for specific answers to a very general question.
Basically, apart of UI operations, you should protect every shared memory/resource access to avoid two potentially competing threads to:
read inconsistent memory
write memory at the same time
try to use the same resource at the same time from more than one thread... until the resource is thread-safe.
Generally, I consider any other operation thread safe, including operations that access not shared memory or not shared objects.
For example, consider this object:
type
TThrdExample = class
private
FValue: Integer;
public
procedure Inc;
procedure Dec;
function Value: Integer;
procedure ThreadInc;
procedure ThreadDec;
function ThreadValue: Integer;
end;
ThreadVar
ThreadValue: Integer;
Inc, Dec and Value are methods which operate over FValue field. The methods are not thread safe until you protect them with some synchronization mechanism. It can be a MultipleReaderExclusiveWriterSinchronizer for Value function and CriticalSection for Inc and Dec methods.
ThreadInc and ThreadDec methods operate over ThreadValue variable, which is defined as ThreadVar, so I consider it ThreadSafe because the memory they access is not shared between threads... each call from different thread will access different memory address.
If you know that, by design, a class should be used only in one thread or inside other synchronization mechanisms, you're free to consider that thread safe by design.
If you want more specific answers, I suggest you try with a more specific question.
Best regards.
EDIT: Maybe someone say the integer fields is a bad example because you can consider integer operations atomic on Intel/Windows thus is not needed to protect it... but I hope you get the idea.
You misunderstood TThread.Synchronize method.
TThread.Synchronize and TThread.Queue methods executes protected code in the context of main (GUI) thread. That is why you should use Syncronize or Queue to update GUI controls (like progressbar) - normally only main thread should access GUI controls.
Critical Sections are different - the protected code is executed in the context of the thread that acquired critical section, and no other thread is permitted to acquire the critical section until the former thread releases it.
You use critical section in case there's a need for a certain set of objects to be updated atomically. This means, they must at all times be either already updated completely or not yet updated at all. They must never be accessible in a transitional state.
For example, with a simple integer reading/writing this is not the case. The operation of reading integer as well as the operation of writing it are atomic already: you cannot read integer in the middle of processor writing it, half-updated. It's either old value or new value, always.
But if you want to increment the integer atomically, you have not one, but three operations you have to do at once: read the old value into processor's cache, increment it, and write it back to memory. Each operation is atomic, but the three of them together are not.
One thread might read the old value (say, 200), increment it by 5 in cache, and at the same time another thread might read the value too (still 200). Then the first thread writes back 205, while the second thread increments its cached value of 200 to 203 and writes back 203, overwriting 205. The result of two increments (+5 and +3) should be 208, but it's 203 due to non-atomicity of operations.
So, you use critical sections when:
A variable, set of variables, or any resource is used from several threads and needs to be updated atomically.
It's not atomic by itself (for example, calling a function which is guarded by critical section inside of the function body, is an atomic operation already)
Have a read of this documentation
http://www.eonclash.com/Tutorials/Multithreading/MartinHarvey1.1/ToC.html
If you use messaging to communicate between threads then you can basically ignore synchronisation primitives completely because each thread only accesses its internal structures and the messages themselves. In essence this is far easier and more scalable architecture than using synchronisation primitives.
While reading C# 3.0 in a Nutshell by Joseph and Ben Albahari, I came across the following paragraph (page 673, first paragraph in section titled "Signaling with Wait and Pulse")
"The Monitor class provides another signalling construct via two static methods, Wait and Pulse. The principle is that you write the signalling logic yourself using custom flags and fields (enclosed in lock statements), and then introduce Wait and Pulse commands to mitigate CPU spinning. The advantage of this low-level approach is that with just Wait, Pulse, and the lock statement, you can achieve the functionality of AutoResetEvent, ManualResetEvent, and Semaphore, as well as WaitHandle's static methods WaitAll and WaitAny. Furthermore, Wait and Pulse
can be amenable in situations where
all of the wait handles are
parsimoniously challenged."
My question is, what is the correct interpretation of the last sentence?
A situation with a decent/large number of wait handles where WaitOne() is only occasionally called on any particular wait handle.
A situation with a decent/large number of wait handles where rarely does more than one thread tend to block on any particular wait handle.
Some other interpretation.
Would also appreciate illuminating examples of such situations and perhaps how and/or why they are more efficiently handled via Wait and Pulse rather than by other methods.
Thank you!
Edit: I found the text online here
What this is saying is that there are some situations where Wait and Pulse provides a simpler solution than wait handles. In general, this happens where:
The waiter, rather than the notifier, decides when to unblock
The blocking condition involves more than a simple flag (perhaps several variables)
You can still use wait handles in these situations, but Wait/Pulse tends to be simpler. The great thing about Wait/Pulse is that Wait releases the underlying lock while waiting. For instance, in the following example, we're reading _x and _y within the safety of a lock - and yet that lock is released while waiting so that another thread can update those variables:
lock (_locker)
{
while (_x < 10 && _y < 20) Monitor.Wait (_locker);
}
Another thread can then update _x and _y atomically (by virtue of the lock) and then Pulse to signal the waiter:
lock (_locker)
{
_x = 20;
_y = 30;
Monitor.Pulse (_locker);
}
The disadvantage of Wait/Pulse is that it's easier to get it wrong and make a mistake (for instance, by updating a variable and forgetting to Pulse). In situations where a program with wait handles is equally simple to a program with Wait/Pulse, I'd recommend going with wait handles for that reason.
In terms of efficiency/resource consumption (which I think you were alluding to), Wait/Pulse is usually faster and lighter (as it has a managed implementation). This is rarely a big deal in practice, though. And on that point, Framework 4.0 includes low-overhead managed versions of ManualResetEvent and Semaphore (ManualResetEventSlim and SemaphoreSlim).
Framework 4.0 also provides many more synchronization options that lessen the need for Wait/Pulse:
CountdownEvent
Barrier
PLINQ / Data Parallelism (AsParallel, Parallel.Invoke, Parallel.For, Parallel.ForEach)
Tasks and continuations
All of these are much higher-level than Wait/Pulse and IMO are preferable for writing reliable and maintainable code (assuming they'll solve the task at hand).
Could you describe two methods of synchronizing multi-threaded write access performed
on a class member?
Please could any one help me what is this meant to do and what is the right answer.
When you change data in C#, something that looks like a single operation may be compiled into several instructions. Take the following class:
public class Number {
private int a = 0;
public void Add(int b) {
a += b;
}
}
When you build it, you get the following IL code:
IL_0000: nop
IL_0001: ldarg.0
IL_0002: dup
// Pushes the value of the private variable 'a' onto the stack
IL_0003: ldfld int32 Simple.Number::a
// Pushes the value of the argument 'b' onto the stack
IL_0008: ldarg.1
// Adds the top two values of the stack together
IL_0009: add
// Sets 'a' to the value on top of the stack
IL_000a: stfld int32 Simple.Number::a
IL_000f: ret
Now, say you have a Number object and two threads call its Add method like this:
number.Add(2); // Thread 1
number.Add(3); // Thread 2
If you want the result to be 5 (0 + 2 + 3), there's a problem. You don't know when these threads will execute their instructions. Both threads could execute IL_0003 (pushing zero onto the stack) before either executes IL_000a (actually changing the member variable) and you get this:
a = 0 + 2; // Thread 1
a = 0 + 3; // Thread 2
The last thread to finish 'wins' and at the end of the process, a is 2 or 3 instead of 5.
So you have to make sure that one complete set of instructions finishes before the other set. To do that, you can:
1) Lock access to the class member while it's being written, using one of the many .NET synchronization primitives (like lock, Mutex, ReaderWriterLockSlim, etc.) so that only one thread can work on it at a time.
2) Push write operations into a queue and process that queue with a single thread. As Thorarin points out, you still have to synchronize access to the queue if it isn't thread-safe, but it's worth it for complex write operations.
There are other techniques. Some (like Interlocked) are limited to particular data types, and there are even more (like the ones discussed in Non-blocking synchronization and Part 4 of Joseph Albahari's Threading in C#), though they are more complex: approach them with caution.
In multithreaded applications, there are many situations where simultaneous access to the same data can cause problems. In such cases synchronization is required to guarantee that only one thread has access at any one time.
I imagine they mean using the lock-statement (or SyncLock in VB.NET) vs. using a Monitor.
You might want to read this page for examples and an understanding of the concept. However, if you have no experience with multithreaded application design, it will likely become quickly apparent, should your new employer put you to the test. It's a fairly complicated subject, with many possible pitfalls such as deadlock.
There is a decent MSDN page on the subject as well.
There may be other options, depending on the type of member variable and how it is to be changed. Incrementing an integer for example can be done with the Interlocked.Increment method.
As an excercise and demonstration of the problem, try writing an application that starts 5 simultaneous threads, incrementing a shared counter a million times per thread. The intended end result of the counter would be 5 million, but that is (probably) not what you will end up with :)
Edit: made a quick implementation myself (download). Sample output:
Unsynchronized counter demo:
expected counter = 5000000
actual counter = 4901600
Time taken (ms) = 67
Synchronized counter demo:
expected counter = 5000000
actual counter = 5000000
Time taken (ms) = 287
There are a couple of ways, several of which are mentioned previously.
ReaderWriterLockSlim is my preferred method. This gives you a database type of locking, and allows for upgrading (although the syntax for that is incorrect in the MSDN last time I looked and is very non-obvious)
lock statements. You treat a read like a write and just prevent access to the variable
Interlocked operations. This performs an operations on a value type in an atomic step. This can be used for lock free threading (really wouldn't recommend this)
Mutexes and Semaphores (haven't used these)
Monitor statements (this is essentially how the lock keyword works)
While I don't mean to denigrate other answers, I would not trust anything that does not use one of these techniques. My apologies if I have forgotten any.
I understand about race conditions and how with multiple threads accessing the same variable, updates made by one can be ignored and overwritten by others, but what if each thread is writing the same value (not different values) to the same variable; can even this cause problems? Could this code:
GlobalVar.property = 11;
(assuming that property will never be assigned anything other than 11), cause problems if multiple threads execute it at the same time?
The problem comes when you read that state back, and do something about it. Writing is a red herring - it is true that as long as this is a single word most environments guarantee the write will be atomic, but that doesn't mean that a larger piece of code that includes this fragment is thread-safe. Firstly, presumably your global variable contained a different value to begin with - otherwise if you know it's always the same, why is it a variable? Second, presumably you eventually read this value back again?
The issue is that presumably, you are writing to this bit of shared state for a reason - to signal that something has occurred? This is where it falls down: when you have no locking constructs, there is no implied order of memory accesses at all. It's hard to point to what's wrong here because your example doesn't actually contain the use of the variable, so here's a trivialish example in neutral C-like syntax:
int x = 0, y = 0;
//thread A does:
x = 1;
y = 2;
if (y == 2)
print(x);
//thread B does, at the same time:
if (y == 2)
print(x);
Thread A will always print 1, but it's completely valid for thread B to print 0. The order of operations in thread A is only required to be observable from code executing in thread A - thread B is allowed to see any combination of the state. The writes to x and y may not actually happen in order.
This can happen even on single-processor systems, where most people do not expect this kind of reordering - your compiler may reorder it for you. On SMP even if the compiler doesn't reorder things, the memory writes may be reordered between the caches of the separate processors.
If that doesn't seem to answer it for you, include more detail of your example in the question. Without the use of the variable it's impossible to definitively say whether such a usage is safe or not.
It depends on the work actually done by that statement. There can still be some cases where Something Bad happens - for example, if a C++ class has overloaded the = operator, and does anything nontrivial within that statement.
I have accidentally written code that did something like this with POD types (builtin primitive types), and it worked fine -- however, it's definitely not good practice, and I'm not confident that it's dependable.
Why not just lock the memory around this variable when you use it? In fact, if you somehow "know" this is the only write statement that can occur at some point in your code, why not just use the value 11 directly, instead of writing it to a shared variable?
(edit: I guess it's better to use a constant name instead of the magic number 11 directly in the code, btw.)
If you're using this to figure out when at least one thread has reached this statement, you could use a semaphore that starts at 1, and is decremented by the first thread that hits it.
I would expect the result to be undetermined. As in it would vary from compiler to complier, langauge to language and OS to OS etc. So no, it is not safe
WHy would you want to do this though - adding in a line to obtain a mutex lock is only one or two lines of code (in most languages), and would remove any possibility of problem. If this is going to be two expensive then you need to find an alternate way of solving the problem
In General, this is not considered a safe thing to do unless your system provides for atomic operation (operations that are guaranteed to be executed in a single cycle).
The reason is that while the "C" statement looks simple, often there are a number of underlying assembly operations taking place.
Depending on your OS, there are a few things you could do:
Take a mutual exclusion semaphore (mutex) to protect access
in some OS, you can temporarily disable preemption, which guarantees your thread will not swap out.
Some OS provide a writer or reader semaphore which is more performant than a plain old mutex.
Here's my take on the question.
You have two or more threads running that write to a variable...like a status flag or something, where you only want to know if one or more of them was true. Then in another part of the code (after the threads complete) you want to check and see if at least on thread set that status... for example
bool flag = false
threadContainer tc
threadInputs inputs
check(input)
{
...do stuff to input
if(success)
flag = true
}
start multiple threads
foreach(i in inputs)
t = startthread(check, i)
tc.add(t) // Keep track of all the threads started
foreach(t in tc)
t.join( ) // Wait until each thread is done
if(flag)
print "One of the threads were successful"
else
print "None of the threads were successful"
I believe the above code would be OK, assuming you're fine with not knowing which thread set the status to true, and you can wait for all the multi-threaded stuff to finish before reading that flag. I could be wrong though.
If the operation is atomic, you should be able to get by just fine. But I wouldn't do that in practice. It is better just to acquire a lock on the object and write the value.
Assuming that property will never be assigned anything other than 11, then I don't see a reason for assigment in the first place. Just make it a constant then.
Assigment only makes sense when you intend to change the value unless the act of assigment itself has other side effects - like volatile writes have memory visibility side-effects in Java. And if you change state shared between multiple threads, then you need to synchronize or otherwise "handle" the problem of concurrency.
When you assign a value, without proper synchronization, to some state shared between multiple threads, then there's no guarantees for when the other threads will see that change. And no visibility guarantees means that it it possible that the other threads will never see the assignt.
Compilers, JITs, CPU caches. They're all trying to make your code run as fast as possible, and if you don't make any explicit requirements for memory visibility, then they will take advantage of that. If not on your machine, then somebody elses.