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Does 'volatile' guarantee that any thread reads the most recently written value?

From the book Effective Java:
While the volatile modifier performs no mutual exclusion, it guarantees that any thread that reads the field will see the most recently written value
SO and many other sources claim similar things.
Is this true?
I mean really true, not a close-enough model, or true only on x86, or only in Oracle JVMs, or some definition of "most recently written" that's not the standard English interpretation...
Other sources (SO example) have said that volatile in Java is like acquire/release semantics in C++. Which I think do not offer the guarantee from the quote.
I found that in the JLS 17.4.4 it says "A write to a volatile variable v (§8.3.1.4) synchronizes-with all subsequent reads of v by any thread (where "subsequent" is defined according to the synchronization order)." But I don't quite understand.
There are quite some sources for and against this, so I'm hoping the answer is able to convince that many of those (on either side) are indeed wrong - for example reference or spec, or counter-example code.

Is this true?
I mean really true, not a close-enough model, or true only on x86, or only in Oracle JVMs, or some definition of "most recently written" that's not the standard English interpretation...
Yes, at least in the sense that a correct implementation of Java gives you this guarantee.
Unless you are using some exotic, experimental Java compiler/JVM (*), you can essentially take this as true.
From JLS 17.4.5:
A write to a volatile field (§8.3.1.4) happens-before every subsequent read of that field.
(*) As Stephen C points out, such an exotic implementation that doesn't implement the memory model semantics described in the language spec can't usefully (or even legally) be described as "Java".

The quote per-se is correct in terms of what is tries to prove, but it is incorrect on a broader view.
It tries to make a distinction of sequential consistency and release/acquire semantics, at least in my understanding. The difference is rather "thin" between these two terms, but very important. I have tried to simplify the difference at the beginning of this answer or here.
The author is trying to say that volatile offers that sequential consistency, as implied by that:
"... it guarantees that any thread.."
If you look at the JLS, it has this sentence:
A write to a volatile field (§8.3.1.4) happens-before every subsequent read of that field.
The tricky part there is that subsequent and it's meaning, and it has been discussed here. What is really wants to mean is "subsequent that observes that write". So happens-before is guaranteed when the reader observes the value that the writer has written.
This already implies that a write is not necessarily seen on the next read, and this can be the case where speculative execution is allowed. So in this regard, the quote is miss-leading.
The quote that you found:
A write to a volatile variable v (§8.3.1.4) synchronizes-with all subsequent reads of v by any thread (where "subsequent" is defined according to the synchronization order)
is a complicated to understand without a much broader context. In simple words, it established synchronizes-with order (and implicitly happens-before) between two threads, where volatile v variables is a shared variable. here is an answer where this has broader explanation and thus should make more sense.

It is not true. JMM is based on sequential consistency and for sequential consistency real time ordering isn't guaranteed; for that you need linearizability. In other words, reads and writes can be skewed as long as the program order isn't violated (or as long is it can't be proven po was violated).
A read of volatile variable a, needs to see the most recent written value before it in the memory order. But that doesn't imply real time ordering.
Good read about the topic:
https://concurrency-interest.altair.cs.oswego.narkive.com/G8KjyUtg/relativity-of-guarantees-provided-by-volatile.
I'll make it concrete:
Imagine there are 2 CPU's and (volatile) variable A with initial value 0. CPU1 does a store A=1 and CPU2 does a load of A. And both CPUs have the cacheline containing A in SHARED state.
The store is first speculatively executed and written to the store buffer; eventually the store commits and retires, but since the stored value is still in the store buffer; it isn't visible yet to the CPU2. Till so far it wasn't required for the cacheline to be in an EXCLUSIVE/MODIFIED state, so the cacheline on CPU2 still contains the old value and hence CPU2 can still read the old value.
So in the real time order, the write of A is ordered before the read of A=0, but in the synchronization order, the write of A=1 is ordered after the read of A=0.
Only when the store leaves the store buffer and wants to enter the L1 cache, the request for ownership (RFO) is send to all other CPU's which set the cacheline containing A to INVALID on CPU2 (RFO prefetching I'll leave out of the discussion). If CPU2 would now read A, it is guaranteed to see A=1 (the request will block till CPU1 has completed the store to the L1 cache).
On acknowledgement of the RFO the cacheline is set to MODIFIED on CPU1 and the store is written to the L1 cache.
So there is a period of time between when the store is executed/retired and when it is visible to another CPU. But the only way to determine this is if you would add special measuring equipment to the CPUs.
I believe a similar delaying effect can happen on the reading side with invalidation queues.
In practice this will not be an issue because store buffers have a limited capacity and need to be drained eventually (so a write can't be invisible indefinitely). So in day to day usage you could say that a volatile read, reads the most recent write.
A java volatile write/read provides release/acquire semantics, but keep in mind that the volatile write/read is stronger than release/acquire semantics. A volatile write/read is sequential consistent and release/acquire semantics isn't.

Memory Barrier Vs CAS

I find that CAS will flush all CPU write cache to main memory。 Is this similar to memory barrier？
If this is true, does this mean CAS can make java Happens-Before work?
For answer：
The CAS is CPU instruction.
The barrier is a StoreLoad barrier because what I care about is will the data are written before CAS can be read after CAS.
More Detail:
I have this question because I am writing a fork-join built in Java. The implementation is like this
{
//initialize result container
Objcet[] result = new Object[];
//worker finish state count
AtomicInteger state = new AtomicInteger(result.size);
}
//worker thread i
{
result[i] = new Object();
//this is a CAS operation
state.getAndDecrement();
if(state.get() == 0){
//do something useing result array
}
}
I want to know can (do something using result array) part see all result element which is written by other worker thread.

I find that CAS will flush all cpu write cache to main memory。 Is this similar to memory barrier？
It depends on what you mean by CAS. (A specific hardware instruction? An implementation strategy used in the implementation of some Java class?)
It depends on what kind of memory barrier you are talking about. There are a number of different kinds ...
It is not necessarily true that a CAS instruction flushes all dirty cache lines. It depends on how a particular instruction set / hardware implements the CAS instruction.
It is unclear what you mean by "make happens-before work". Certainly, under some circumstance a CAS instruction would provide the necessary memory coherency properties for a specific happens-before relationship. But not necessarily all relationships. It would depend on how the CAS instruction is implemented by the hardware.
To be honest, unless you are actually writing a Java compiler, you would do better to not try to understanding the intricacies of what a JIT compiler needs to do to implement the Java Memory Model. Just apply the happens before rules.
UPDATE
It turns out from your recent updates and comments that your actual question is about the behavior of AtomicInteger operations.
The memory semantics of the atomic types are specified in the package javadoc for java.util.concurrent.atomic as follows:
The memory effects for accesses and updates of atomics generally follow the rules for volatiles, as stated in The Java Language Specification (17.4 Memory Model):
get has the memory effects of reading a volatile variable.
set has the memory effects of writing (assigning) a volatile variable.
lazySet has the memory effects of writing (assigning) a volatile variable except that it permits reorderings with subsequent (but not previous) memory actions that do not themselves impose reordering constraints with ordinary non-volatile writes. Among other usage contexts, lazySet may apply when nulling out, for the sake of garbage collection, a reference that is never accessed again.
weakCompareAndSet atomically reads and conditionally writes a variable but does not create any happens-before orderings, so provides no guarantees with respect to previous or subsequent reads and writes of any variables other than the target of the weakCompareAndSet.
compareAndSet and all other read-and-update operations such as getAndIncrement have the memory effects of both reading and writing volatile variables.
As you can see, operations on Atomic types are specified to have memory semantics that are equivalent volatile variables. This should be sufficient to reason about your use of Java atomic types ... without resorting to dubious analogies with CAS instructions and memory barriers.
Your example is incomplete and it is difficult to understand what it is trying to do. Therefore, I can't comment on its correctness. However, you should be able to analyze it yourself using happens-before logic, etc.

I find that CAS will flush all CPU write cache to main memory。
Is this similar to memory barrier？
A CAS in Java on the X86 is implemented using a lock prefix and then it depends on the type of CAS what kind of instruction is actually being used; but that isn't that relevant for this discussion. A locked instruction effectively is a full barrier; so it includes all 4 fences: LoadLoad/LoadStore/StoreLoad/StoreStore. Since the X86 provides all but StoreLoad due to TSO, only the StoreLoad needs to be added; just as with a volatile write.
A StoreLoad doesn't force changes to be written to main memory; it only forces the CPU to wait executing loads till the store buffer has been be drained to the L1d. However, with MESI (Intel) based cache coherence protocols, it can happen that a cache-line that is in MODIFIED state on a different CPU, needs to be flushed to main memory before it can be returned as EXCLUSIVE. With MOESI (AMD) based cache coherence protocols, this is not an issue. If the cache-line is already in MODIFIED,EXCLUSIVE state on the core doing the StoreLoad, StoreLoad doesn't cause the cache line to be flushed to main memory. The cache is the source of truth.
If this is true, does this mean CAS can make java Happens-Before work?
From a memory model perspective, a successful CAS in java is nothing else than a volatile read followed by a volatile write. So there is a happens before relation between a volatile write of some field on some object instance and a subsequent volatile read on the same field on the same object instance.
Since you are working with Java, I would focus on the Java Memory Model and not too much on how it is implemented in the hardware. The JMM is allowing for executions that can't be explained based purely by thinking in fences.
Regarding your example:
result[i] = new Object();
//this is a CAS operation
state.getAndDecrement();
if(state.get() == 0){
//do something using result array
}
I'm not sure what the intended logic is. In your example, multiple threads at the same time could see that the state is 0, so all could start to do something with the array. If this behavior is undesirable, then this is caused by a race condition. I would use something like this:
result[i] = new Object();
//this is a CAS operation
int s = state.getAndDecrement();
if(s == 0){
//do something using result array
}
Now the other question is if there is a data race on the array content. There is a happens-before edge between the write to the array content and the write to 'state' (program order rule). There is a happens before edge between the write of the state and the read (volatile variable rule) and there is a happens before relation between the read of the state and the read of the array content (program order rule). So there is a happens before edge between writing to the array and reading its content in this particular example due to the transitive nature of the happens-before relation.
Personally I would not to try too be too smart and use something less array prone like an AtomicReferenceArray; then at least you don't need to worry about missing happens before edge between the write of the array and the read.

VarHandle get/setOpaque

I keep fighting to understand what VarHandle::setOpaque and VarHandle::getOpaque are really doing. It has not been easy so far - there are some things I think I get (but will not present them in the question itself, not to muddy the waters), but overall this is miss-leading at best for me.
The documentation:
Returns the value of a variable, accessed in program order...
Well in my understanding if I have:
int xx = x; // read x
int yy = y; // read y
These reads can be re-ordered. On the other had if I have:
// simplified code, does not compile, but reads happen on the same "this" for example
int xx = VarHandle_X.getOpaque(x);
int yy = VarHandle_Y.getOpaque(y);
This time re-orderings are not possible? And this is what it means "program order"? Are we talking about insertions of barriers here for this re-ordering to be prohibited? If so, since these are two loads, would the same be achieved? via:
int xx = x;
VarHandle.loadLoadFence()
int yy = y;
But it gets a lot trickier:
... but with no assurance of memory ordering effects with respect to other threads.
I could not come up with an example to even pretend I understand this part.
It seems to me that this documentation is targeted at people who know exactly what they are doing (and I am definitely not one)... So can someone shed some light here?

Well in my understanding if I have:
int xx = x; // read x
int yy = y; // read y
These reads can be re-ordered.
These reads may not only happen to be reordered, they may not happen at all. The thread may use an old, previously read value for x and/or y or values it did previously write to these variables whereas, in fact, the write may not have been performed yet, so the “reading thread” may use values, no other thread may know of and are not in the heap memory at that time (and probably never will).
On the other had if I have:
// simplified code, does not compile, but reads happen on the same "this" for example
int xx = VarHandle_X.getOpaque(x);
int yy = VarHandle_Y.getOpaque(y);
This time re-orderings are not possible? And this is what it means "program order"?
Simply said, the main feature of opaque reads and writes, is, that they will actually happen. This implies that they can not be reordered in respect to other memory access of at least the same strength, but that has no impact for ordinary reads and writes.
The term program order is defined by the JLS:
… the program order of t is a total order that reflects the order in which these actions would be performed according to the intra-thread semantics of t.
That’s the evaluation order specified for expressions and statements. The order in which we perceive the effects, as long as only a single thread is involved.
Are we talking about insertions of barriers here for this re-ordering to be prohibited?
No, there is no barrier involved, which might be the intention behind the phrase “…but with no assurance of memory ordering effects with respect to other threads”.
Perhaps, we could say that opaque access works a bit like volatile was before Java 5, enforcing read access to see the most recent heap memory value (which makes only sense if the writing end also uses opaque or an even stronger mode), but with no effect on other reads or writes.
So what can you do with it?
A typical use case would be a cancellation or interruption flag that is not supposed to establish a happens-before relationship. Often, the stopped background task has no interest in perceiving actions made by the stopping task prior to signalling, but will just end its own activity. So writing and reading the flag with opaque mode would be sufficient to ensure that the signal is eventually noticed (unlike the normal access mode), but without any additional negative impact on the performance.
Likewise, a background task could write progress updates, like a percentage number, which the reporting (UI) thread is supposed to notice timely, while no happens-before relationship is required before the publication of the final result.
It’s also useful if you just want atomic access for long and double, without any other impact.
Since truly immutable objects using final fields are immune to data races, you can use opaque modes for timely publishing immutable objects, without the broader effect of release/acquire mode publishing.
A special case would be periodically checking a status for an expected value update and once available, querying the value with a stronger mode (or executing the matching fence instruction explicitly). In principle, a happens-before relationship can only be established between the write and its subsequent read anyway, but since optimizers usually don’t have the horizon to identify such a inter-thread use case, performance critical code can use opaque access to optimize such scenario.

The opaque means that the thread executing opaque operation is guaranteed to observe its own actions in program order, but that's it.
Other threads are free to observe the threads actions in any order. On x86 it is a common case since it has
write ordered with store-buffer forwarding
memory model so even if the thread does store before load. The store can be cached in the store buffer and some thread being executed on any other core observes the thread action in reverse order load-store instead of store-load. So opaque operation is done on x86 for free (on x86 we actually also have acquire for free, see this extremely exhaustive answer for details on some other architectures and their memory models: https://stackoverflow.com/a/55741922/8990329)
Why is it useful? Well, I could speculate that if some thread observed a value stored with opaque memory semantic then subsequent read will observe "at least this or later" value (plain memory access does not provide such guarantees, does it?).
Also since Java 9 VarHandles are somewhat related to acquire/release/consume semantic in C I think it is worth noting that opaque access is similar to memory_order_relaxed which is defined in the Standard as follows:
For memory_order_relaxed, no operation orders memory.
with some examples provided.

I have been struggling with opaque myself and the documentation is certainly not easy to understand.
From the above link:
Opaque operations are bitwise atomic and coherently ordered.
The bitwise atomic part is obvious. Coherently ordered means that loads/stores to a single address have some total order, each reach sees the most recent address before it and the order is consistent with the program order. For some coherence examples, see the following JCStress test.
Coherence doesn't provide any ordering guarantees between loads/stores to different addresses so it doesn't need to provide any fences so that loads/stores to different addresses are ordered.
With opaque, the compiler will emit the loads/stores as it sees them. But the underlying hardware is still allowed to reorder load/stores to different addresses.
I upgraded your example to the message-passing litmus test:
thread1:
X.setOpaque(1);
Y.setOpaque(1);
thread2:
ry = Y.getOpaque();
rx = X.getOpaque();
if (ry == 1 && rx == 0) println("Oh shit");
The above could fail on a platform that would allow for the 2 stores to be reordered or the 2 loads (again ARM or PowerPC). Opaque is not required to provide causality. JCStress has a good example for that as well.
Also, the following IRIW example can fail:
thread1:
X.setOpaque(1);
thread2:
Y.setOpaque(1);
thread3:
rx_thread3 = X.getOpaque();
[LoadLoad]
ry_thread3 = Y.getOpaque();
thread4:
ry_thread4 = Y.getOpaque();
[LoadLoad]
rx_thread4 = X.getOpaque();
Can it be that we end up with rx_thread3=1,ry_thread3=0,ry_thread4=1 and rx_thread4 is 0?
With opaque this can happen. Even though the loads are prevented from being reordered, opaque accesses do not require multi-copy-atomicity (stores to different addresses issued by different CPUs can be seen in different orders).
Release/acquire is stronger than opaque, since with release/acquire it is allowed to fail, therefor with opaque, it is allowed to fail. So Opaque is not required to provide consensus.

Java volatile effect on other variables [duplicate]

So I am reading this book titled Java Concurrency in Practice and I am stuck on this one explanation which I cannot seem to comprehend without an example. This is the quote:
When thread A writes to a volatile
variable and subsequently thread B
reads that same variable, the values
of all variables that were visible to
A prior to writing to the volatile
variable become visible to B after
reading the volatile variable.
Can someone give me a counterexample of why "the values of ALL variables that were visible to A prior to writing to the volatile variable become visible to B AFTER reading the volatile variable"?
I am confused why all other non-volatile variables do not become visible to B before reading the volatile variable?

Declaring a volatile Java variable means:
The value of this variable will never be cached thread-locally: all reads and writes will go straight to "main memory".
Access to the variable acts as though it is enclosed in a synchronized block, synchronized on itself.
Just for your reference, When is volatile needed ?
When multiple threads using the same
variable, each thread will have its
own copy of the local cache for that
variable. So, when it's updating the
value, it is actually updated in the
local cache not in the main variable
memory. The other thread which is
using the same variable doesn't know
anything about the values changed by
the another thread. To avoid this
problem, if you declare a variable as
volatile, then it will not be stored
in the local cache. Whenever thread
are updating the values, it is updated
to the main memory. So, other threads
can access the updated value.
From JLS §17.4.7 Well-Formed Executions
We only consider well-formed
executions. An execution E = < P, A,
po, so, W, V, sw, hb > is well formed
if the following conditions are true:
Each read sees a write to the same
variable in the execution. All reads
and writes of volatile variables are
volatile actions. For all reads r in
A, we have W(r) in A and W(r).v = r.v.
The variable r.v is volatile if and
only if r is a volatile read, and the
variable w.v is volatile if and only
if w is a volatile write.
Happens-before order is a partial
order. Happens-before order is given
by the transitive closure of
synchronizes-with edges and program
order. It must be a valid partial
order: reflexive, transitive and
antisymmetric.
The execution obeys
intra-thread consistency. For each
thread t, the actions performed by t
in A are the same as would be
generated by that thread in
program-order in isolation, with each
write wwriting the value V(w), given
that each read r sees the value
V(W(r)). Values seen by each read are
determined by the memory model. The
program order given must reflect the
program order in which the actions
would be performed according to the
intra-thread semantics of P.
The execution is happens-before consistent
(§17.4.6).
The execution obeys
synchronization-order consistency. For
all volatile reads r in A, it is not
the case that either so(r, W(r)) or
that there exists a write win A such
that w.v = r.v and so(W(r), w) and
so(w, r).
Useful Link : What do we really know about non-blocking concurrency in Java?

Thread B may have a CPU-local cache of those variables. A read of a volatile variable ensures that any intermediate cache flush from a previous write to the volatile is observed.
For an example, read the following link, which concludes with "Fixing Double-Checked Locking using Volatile":
http://www.cs.umd.edu/~pugh/java/memoryModel/DoubleCheckedLocking.html

If a variable is non-volatile, then the compiler and the CPU, may re-order instructions freely as they see fit, in order to optimize for performance.
If the variable is now declared volatile, then the compiler no longer attempts to optimize accesses (reads and writes) to that variable. It may however continue to optimize access for other variables.
At runtime, when a volatile variable is accessed, the JVM generates appropriate memory barrier instructions to the CPU. The memory barrier serves the same purpose - the CPU is also prevent from re-ordering instructions.
When a volatile variable is written to (by thread A), all writes to any other variable are completed (or will atleast appear to be) and made visible to A before the write to the volatile variable; this is often due to a memory-write barrier instruction. Likewise, any reads on other variables, will be completed (or will appear to be) before the
read (by thread B); this is often due to a memory-read barrier instruction. This ordering of instructions that is enforced by the barrier(s), will mean that all writes visible to A, will be visible B. This however, does not mean that any re-ordering of instructions has not happened (the compiler may have performed re-ordering for other instructions); it simply means that if any writes visible to A have occurred, it would be visible to B. In simpler terms, it means that strict-program order is not maintained.
I will point to this writeup on Memory Barriers and JVM Concurrency, if you want to understand how the JVM issues memory barrier instructions, in finer detail.
Related questions
What is a memory fence?
What are some tricks that a processor does to optimize code?

Threads are allowed to cache variable values that other threads may have since updated since they read them. The volatile keyword forces all threads to not cache values.

This is simply an additional bonus the memory model gives you, if you work with volatile variables.
Normally (i.e. in the absence of volatile variables and synchronization), the VM can make variables from one thread visible to other threads in any order it wants, or not at all. E.g. the reading thread could read some mixture of earlier versions of another threads variable assignments. This is caused by the threads being maybe run on different CPUs with their own caches, which are only sometimes copied to the "main memory", and additionally by code reordering for optimization purposes.
If you used a volatile variable, as soon as thread B read some value X from it, the VM makes sure that anything which thread A has written before it wrote X is also visible to B. (And also everything which A got guaranteed as visible, transitively).
Similar guarantees are given for synchronized blocks and other types of locks.

Does volatile influence non-volatile variables?

Okay, suppose I have a bunch of variables, one of them declared volatile:
int a;
int b;
int c;
volatile int v;
If one thread writes to all four variables (writing to v last), and another thread reads from all four variables (reading from v first), does that second thread see the values written to a, b and c by the first thread, even though they are not themselves declared volatile? Or can it possibly see stale values?
Since there seems to be some confusion: I'm not deliberately trying to do something unsafe. I just want to understand the Java memory model and the semantics of the volatile keyword. Pure curiosity.

I'm going to speak to what I think you may really be probing about—piggybacking synchronization.
The technique that it looks like you're trying to use involves using one volatile variable as a synchronization guard in concert with one or more other non-volatile variables. This technique is applicable when the following conditions hold true:
Only one thread will write to the set of values meant to be guarded.
The threads reading the set of values will read them only if the volatile guard value meets some criteria.
You don't mention the second condition holding true for your example, but we can examine it anyway. The model for the writer is as follows:
Write to all the non-volatile variables, assuming that no other thread will try to read them.
Once complete, write a value to the volatile guard variable that indicates that the readers' criteria is met.
The readers operate as follows:
Read the volatile guard variable at any time, and if its value meets the criteria, then
Read the other non-volatile variables.
The readers must not read the other non-volatile variables if the volatile guard variable does not yet indicate a proper value.
The guard variable is acting as a gate. It's closed until the writer sets it to a particular value, or set of values that all meet the criteria of indicating that the gate is now open. The non-volatile variables are guarded behind the gate. The reader is not permitted to read them until the gate opens. Once the gate is open, the reader will see a consistent view of the set of non-volatile variables.
Note that it is not safe to run this protocol repeatedly. The writer can't keep changing the non-volatile variables once it's opened the gate. At that point, multiple reader threads may be reading those other variables, and they can—though are not guaranteed—see updates to those variables. Seeing some but not all of those updates would yield inconsistent views of the set.
Backing up, the trick here is to control access to a set of variables without either
creating a structure to hold them all, to which an atomic reference could be swapped, um, atomically, or
using a lock to make writing to and reading from the entire set of variables mutually exclusive activities.
Piggybacking on top of the volatile guard variable is a clever stunt—not one to be done casually. Subsequent updates to the program can break the aforementioned fragile conditions, removing the consistency guarantees afforded by the Java memory model. Should you choose to use this technique, document its invariants and requirements in the code clearly.

Yes. volatile, locks, etc., setup the happens-before relationship, but it affects all variables (in the new Java Memory Model (JMM) from Java SE 5/JDK 1.4). Kind of makes it useful for non-primitive volatiles...

does that second thread see the values written to a, b and c by the first thread, even though they are not themselves declared volatile? Or can it possibly see stale values?
You will get stale reads, b/c you can't ensure that the values of a, b, c are the ones set after reading of v. Using state machine (but you need CAS to change the state) is a way to tackle similar issues but it's beyond the scope of the discussion.
Perhaps this part is unclear, after writing to v and reading first from v, you'd get the right results (non-stale reads), the main issue is that if you do
if (v==STATE1){...proceed...}, there is no guarantee some other thread would not be modifying the state of a/b/c. In that case, there will be state reads.
If you modify the a/b/c+v once only you'd get the correct result.
Mastering concurrency and and lock-free structures is a really hard one. Doug Lea has a good book on and most talks/articles of Dr. Cliff Click are a wonderful wealth, if you need something to start digging in.

Yes, volatile write "happens-before" next volatile read on the same variable.
While #seh is right on about consistency problems with multiple variables, there are use cases that less consistency is required.
For example, a writer thread updates some state variables; a reader thread displays them promptly. There's not much relation among the variables, we only care about reading the new values promptly. We could make every state variable volatile. Or we could use only one volatile variable as visibility guard.
However, the saving is only on the paper, performance wise there's hardly any difference. In either version, every state variable must be "flushed" by the writer and "loaded" by the reader. No free lunch.