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28.11.2015
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Using volatile is yet another way (like synchronized, atomic wrapper) of making class thread safe. Below diagram shows that if two threads are run on different processors, then value of sharedVar may be different in different threads. Note that write of normal variables without any synchronization actions, might not be visible to any reading thread (this behavior is called sequential consistency). Before we move on let’s take a look at two important features of locks and synchronization. Mutual Exclusion: It means that only one thread or process can execute a block of code (critical section) at a time.
Visibility: It means that changes made by one thread to shared data are visible to other threads.
The unsung hero, Volatile keyword: - c# threading My mad and blind love with c# threadingWhen I first saw c# threading code, I just falled in love forever.
My mad and blind love with C# threading When I first saw c# threading code, I just falled in love forever. The memory model is a fascinating topic – it touches on hardware, concurrency, compiler optimizations, and even math. The memory model defines what state a thread may see when it reads a memory location modified by other threads. There are two reasons why a read of a non-volatile field may observe a stale value: compiler optimizations and processor optimizations. In concurrent programming, threads can get interleaved in many different ways, resulting in possibly many different outcomes.
The first reason why a non-volatile read may return a stale value has to do with compiler optimizations. If you mark the _loop field as volatile, the compiler will not hoist the read out of the loop.
The code transformation I showed is a close approximation of the optimization done by the CLR JIT compiler, but not completely exact. The full story is that the assembly code emitted by the JIT compiler will store the value test1._loop in the EAX register. From my experience playing around with the current version of the CLR, I get the impression that these kinds of compiler optimizations are not terribly frequent. On some processors, not only must the compiler avoid certain optimizations on volatile reads and writes, it also has to use special instructions.
The mainstream x86 and x64 processors implement a strong memory model where memory access is effectively volatile. For the reasons explained in this section and the previous sections, marking a field as volatile will often incur zero performance penalty on x86 and x64. To understand how volatile and non-volatile memory accesses work, you can imagine each thread as having its own cache. However, in C# all writes are volatile (unlike say in Java), regardless of whether you write to a volatile or a non-volatile field. A volatile write updates the thread’s cache, and then flushes the entire cache to main memory.
Since all C# writes are volatile, you can think of all writes as going straight to main memory. A regular, non-volatile read can read the value from the thread’s cache, rather than from main memory. When you read a non-volatile field in C#, a non-volatile read occurs, and you may see a stale value from the thread’s cache.
Before the volatile read, thread 2 refreshes its entire cache, and then reads the updated value of v: 11.
Note that the thread caches that I described are imaginary – there really is no such thing as a thread cache. One interesting point is that all writes in C# are volatile according to the memory model as documented here and here, and are also presumably implemented as such. You may find it surprising that a volatile read refreshes the entire cache, not just the  read value. The original Java memory model designed in 1995 was based on weak volatile semantics, but was changed in 2004 to strong volatile. I find the explanation based on imaginary thread caches more intuitive than the more commonly used explanation based on operation reordering.
To be even more accurate, you should assume that the thread caches can form an arbitrary large hierarchy, and so you cannot assume that a read is served only from two possible places – main memory or the thread’s cache. I’m not sure I understand what you mean when you say a volatile read refreshes the entire cache, not just the read value.


I’ll consider writing a separate article on what actually happens on x86, since there seems to be interest in the topic. I have a question I can’t seem to get answered; what happens with respect to [ThreadStatic] variables?
For example, if one thread is looping and polling a thread-static field, it is OK to hoist the read out of the loop.
The safe thing to do would be to publish the Test object by writing it into a volatile field.
If the object is published via a volatile field, the reader cannot possibly observe the state of the object before it was initialized by the constructor. I find this particular example very tricky, even trickier than what Vance’s article admits.
Unfortunately, in this area, even experts disagree on what precisely is and what isn’t guaranteed (example), and so I am not going to claim to know.
This topic seems particular useful for out-of-process services that are intended to provide asynchronous handling of multiple tasks (such as for a high-traffic website that relies on a SOA to display aggregated data).
Very accessible, figurative explanation of how the things actually work with a volatile keyword. Speaking of Alahari, here volatile keyword he seems to say the opposite of what you say on the volatile keyword. Thank you very much for explaining this issue, after reading through several websites which only showed C# sources and gave really weak explanation, this article told me exactly what I was looking for. According to your last table’s combination of refresh cache before read and after write, Volatile read is the same as Lock Acquire, Volatile Write is the same as Lock Release. Essentially, volatile has to do with cache freshness, and lock has to do with cache accessibility.
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The Java volatile keyword is used to mark a Java variable as "being stored in main memory". Instructions before and after can be reordered, but the volatile read or write cannot be mixed with these instructions.
Whatever instructions follow a read or write of a volatile variable are guaranteed to happen after the read or write. When a thread writes to a volatile variable, then not just the volatile variable itself is written to main memory. Also all other variables changed by the thread before writing to the volatile variable are also flushed to main memory.
By the time Thread B reads sharedObject.nonVolatile it will see the value written by Thread A. Developers may use this extended visibility guarantee to optimize the visibility of variables between threads.
Instead of declaring each and every variable volatile, only one or a few need be declared volatile. Reading and writing of volatile variables causes the variable to be read or written to main memory.
Thread safe means that a method or class instance can be used by multiple threads at the same time without any problem. If two threads run on different processors each thread may have its own local copy of sharedVar. Although most modern hardware provide good cache coherence therefore most probably the changes in one cache are reflected in other but it’s not a good practice to rely on hardware for to ‘fix’ a faulty application. If we make the blocks of threads that modifies the value of shared variable synchronized only one thread can enter the block and changes made by it will be reflected in the main memory.
As days passed by the power and simplicity of “System.Threading” namespace converted my love in to blind love. For example, if one thread updates a regular non-volatile field, it is possible that another thread reading the field will never observe the new value. But as the example with the infinite loop shows, threads do not just get interleaved – they potentially interact in more complex ways, unless you correctly use locks and volatile fields.
But, if another thread changes the value of the field, this optimization can prevent the reading thread from noticing the updated value. The compiler will know that other threads may be modifying the field, and so it will be careful to avoid optimizations that would result in a read of a stale value.
The loop condition will keep polling the register, and will read test1._loop from memory again. If _loop is volatile, the compiler will instead keep the test1 variable in EAX, and the value of _loop will be re-fetched from memory on each access (by “ptr [eax+4]”). On x86 and x64, often the same assembly code will be generated regardless of whether a field is volatile or not.


So, a volatile field forces the compiler to avoid some high-level optimizations like hoisting a read out of a loop, but otherwise results in the same assembly code as a non-volatile read. LFENCE and SFENCE are apparently not needed on the current architecture, but MFENCE is useful to go around one particular issue: if a core reads a memory location it previously wrote, the read may be served from the store buffer, even though the write has not yet been written to memory. So, it will observe the value  that is really in main memory, and also refresh its cache as a bonus. Threads only appear to have these caches as an artifact of compiler and processor optimizations. The ECMA specification of the C# language actually defines a weaker model where writes are not volatile by default.
I think that you would have to construct a somewhat of a clever case in order for the cache hierarchy to make a difference, though.
But, MESI protocol is used to ensure that if another core reads the written value, the reader will know where to get the most recent copy of the value.
If another thread modifies the thread-static field, it will modify its own copy, not the one that the looping thread is checking. An alternative solution is to write the object it into a regular field under a lock, and then read it under the same lock. I understand that the order of writes to myValue and myValue fields is guaranteed by the strong ordering of writes. However, lock also have an effect of telling other threads to hold on to their access to the cache.
It would be interesting to see how do you use your thread cache model to explain both freshness and accessibility together.
In order to post comments, please make sure JavaScript and Cookies are enabled, and reload the page. If one thread modifies its value the change might not reflect in the original one in the main memory instantly. All other thread trying to enter the block at the same time will be blocked and put to sleep. The processors may not bother to keep those caches coherent by default, and special instructions may be needed to flush and refresh the caches.
If anyone is aware of a case where the hierarchical thread cache model makes a prediction different from the reordering-based model, I would love to hear about it.
Now when a line is in modified state as a result of the write, it would SNOOP (or intercept) reads from other invalid caches to the main memory for the MODIFIED line that it holds and have them update their line with the data from the modified cache line. Any readers who see the reference to the data structure are guaranteed to see its contents in a fully initialized state. In typical cases, you’ll have some sort of a synchronization between the reader and the writer to pass the object anyways, and that synchronization will be sufficient to insert the appropriate barriers. Joe Albahari has a great write up on the topic as well, but uses the instruction reordering terminology. Volatile variables have the visibility features of synchronized but not the atomicity features. I had create a local variable called as “_loop” as shown in the below abstract code snippet. So I started googling to get reasons for it.After long hours of reading, I was stunned that there are two kinds of memory we have to deal when it comes to threading.
Once the thread is again scheduled to run, the same stale EAX register value will be restored, and the loop never terminates.
LD and ST on the other hand may just access the processor’s cache, which is not visible to other processors.
Admittedly, I don’t fully understand all of the details in the hardware that make this guarantee happen.
This makes my head explode (he references this article Volatile reads and writes, and timeliness from Joe Duff) that in the last rows seems to say the same thing. The values of volatile variable will never be cached and all writes and reads will be done to and from the main memory. However, use of volatile is limited to very restricted set of cases as most of the times atomicity is desired.
For example a simple increment statement such as x = x + 1; or x++ seems to be a single operation but is s really a compound read-modify-write sequence of operations that must execute atomically.
See the below visual representation.As the program runs the main thread set the value to false. This updation is done in the local memory of “mainthread” and main the memory, but it does not update the local memory of “SomeThread”.



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