Friday, February 26, 2010

On satisfaction

It is a great feeling when an elegant piece of code comes together. Even if it can’t be compiled yet.

  Parallel.ForEach(nodeQueue as IOmniValueEnumerable)
procedure (const elem: TOmniValue)
childNode: TNode;
node : TNode;
node := TNode(elem.AsObject);
if node.Value = value then begin
nodeResult := node;
else for childNode in node.Children do

It is even a better feeling when a code that seems to be impossible to write, starts to work.

And the best one – that happens when the code is working so well that you are not afraid of releasing it to the public.

Well, make this almost the best. Because there’s something even better – when people call back to tell you that they like using the code and it is helping them to do their work faster and better.

The feeling that cannot be surpassed comes when such happy user says something like: “Thanks for the code, it helps me a lot. There’s an Amazon gift certificate, spend it as you like.”


I can only respond with: “Rico, thanks!”. OmniThreadLibrary 1.05 is dedicated to you.

Thursday, February 25, 2010

OmniThreadLibrary 1.05

As there were no error reports related to OmniThreadLibrary 1.05 RC, I’ve released final 1.05 version just few moments ago. There are almost no changes between the RC and final release – one demo was added and Parallel.Join code was tweaked a little.

You can download OTL 1.05 from the Google Code. Alternatively, you can update SVN trunk (checkout instructions) or checkout the release-1.05 tag.

Support is available on the web discussion forum.

Big rename

Many internal classes and interfaces was renamed. This should not affect most of the users.

  • TOmniBaseStack –> TOmniBaseBoundedStack
  • TOmniStack –> TOmniBoundedStack
  • TOmniBaseQueue –> TOmniBaseBoundedQueue
  • TOmniQueue –> TOmniBoundedQueue
  • IInterfaceDictionary –> IOmniInterfaceDictionary
  • IInterfaceDictionaryEnumerator -> IOmniInterfaceDictionaryEnumerator,
  • TInterfaceDictionaryPair –> TOmniInterfaceDictionaryPair

I’m sorry for that. Some names are badly chosen and some did not follow the OTL naming conventions.

Dynamic lock-free queue

Implemented dynamically allocated, O(1) enqueue and dequeue, threadsafe,  lock-free queue. Class TOmniBaseQueue contains base implementation while TOmniQueue adds observer support. Both classes live in the OtlContainers unit.

Read more about the TOmniQueue: Dynamic lock-free queue – doing it right.

Inverse semaphore

Implemented resource counter with empty state signalling TOmniResourceCount (unit  OtlSync).

Read more: Three steps to the blocking collection: [1] Inverse semaphore.

Blocking collection

New unit OtlCollection which contains blocking collection implementation  TOmniBlockingCollection.

Read more: Three steps to the blocking collection: [3] Blocking collection


New high-level parallelism support (unit OtlParallel). Requires at least Delphi 2009.

Two parallel control structures are supported: for each (with optional aggregator) and join.

The demo for Parallel.ForEach can be found in project 35_ParallelFor. The same code is reprinted near the end of the Three steps to the blocking collection: [3] Blocking collection post.

Parallel.ForEach.Aggregate was described in Parallel.ForEach.Aggreate post and is demoed in project 36_ParallelAggregate.

At the moment ForEach is fairly limited. It can iterate over a range of numbers or over a collection supporting the IOmniValueEnumerable interface (TOmniBlockingCollection, for example). The second limitation will be removed in the future. The plan is to support any collection that implements IEnumerable.

Parallel.Join is very simple code that executes multiple tasks and waits for their completion. It was designed to execute simple tasks that don’t require communication with the owner. It is demoed in project 37_ParallelJoin.


Unit OtlCommon contains new interface IOmniEnvironment and function Environment that returns singleton of this type. Environment can be used to query some basic information on system, process and thread. Some information (for example process and thread affinity) can also be modified using the same interface.

  IOmniAffinity = interface
property AsString: string;
property Count: integer;
property Mask: DWORD;

IOmniProcessEnvironment = interface
property Affinity: IOmniAffinity;
property Memory: TOmniProcessMemoryCounters;
property PriorityClass: TOmniProcessPriorityClass;
property Times: TOmniProcessTimes;

IOmniSystemEnvironment = interface
property Affinity: IOmniAffinity;

IOmniThreadEnvironment = interface
property Affinity: IOmniAffinity;
property ID: cardinal;

IOmniEnvironment = interface
property Process: IOmniProcessEnvironment;
property System: IOmniSystemEnvironment;
property Thread: IOmniThreadEnvironment;

Newer demos are using some parts of the Environment interface. For example, in demo 33_BlockingCollection, process affinity is set with

  Environment.Process.Affinity.Count := inpNumCPU.Value; 

while the demo 35_ParallelFor uses following code fragment to query process affinity

  numTasks := Environment.Process.Affinity.Count; 

Cancellation token

New interface IOmniCancellationToken is used in the Parallel.ForLoop (see post Three steps to the blocking collection: [3] Blocking collection for the example) and in IOmniTaskControl.TerminateWhen.

IOmniTaskControl and IOmniTask implement CancellationToken: IOmniCancellationToken  property which can be used by the task and task controller.

IOmniCancellationToken is just a simple wrapper around the Win32 event primitive and is defined in the OtlSync unit.

  IOmniCancellationToken = interface
procedure Clear;
function IsSignaled: boolean;
procedure Signal;
property Handle: THandle;
end; { IOmniCancellationToken }

Message dispatcher

IOmniTaskControl now implements message dispatching setter in form OnMessage(msgID, handler). Use it to route specific message IDs to specific functions when global TOmniEventMonitor is not used.

An example from one of my applications:

  spmDatabaseConn := CreateTask(
'Playlist Monitor Database Connection')
.SetParameters([serverAddress, serverPort, username, password])
.SetTimer(15*1000, @TSttdbPlaylistDatabaseWorker.CheckDBVersion)
.OnMessage(MSG_DB_ERROR, HandleError)
.OnMessage(MSG_DB_STATUS, HandleDatabaseStatus)
.OnMessage(MSG_DB_VERSION, HandleDatabaseVersion)


Implemented IOmniTaskControl.UserData[]. The application can store any values in this array. It can be accessed via the integer or string index. This storage are can only be access from the task controller side. Access is not thread-safe so you should use it only from one thread or create your own protection mechanism.

Small changes

  • IOmniTask implements Implementor property which points back to the worker instance  (but only if worker is TOmniWorker-based).
  • Refactored and enhanced TOmniValueContainer.
  • TOmniTaskFunction now takes 'const' parameter. 
    TOmniTaskFunction = reference to procedure(const task: IOmniTask).
  • Implemented TOmniValue.IsInteger.

Bugs fixed

  • TOmniEventMonitor.OnTaskUndeliveredMessage was missing 'message' parameter.
  • Set package names and designtime/runtime type in D2009/D2010 packages.

New demos

  • 32_Queue: Stress test for new TOmniBaseQueue and TOmniQueue.
  • 33_BlockingCollection: Stress test for new TOmniBlockingCollection, also demoes  the use of Environment to set process affinity.
  • 34_TreeScan: Parallel tree scan using TOmniBlockingCollection.
  • 35_ParallelFor: Parallel tree scan using Parallel.ForEach (Delphi 2009 and newer).
  • 36_ParallelAggregate: Parallel calculations using Parallel.ForEach.Aggregate  (Delphi 2009 and newer).

Monday, February 22, 2010

Three steps to the blocking collection: [3] Blocking collection

About two months ago I started working on Delphi clone of .NET 4 BlockingCollection. Initial release was completed just before the end of 2009 and I started to write a series of articles on TOmniBlockingCollection in early January but then I got stuck in the dynamic lock-free queue implementation. Instead of writing articles I spent most of my free time working on that code.

Now it is (finally) time to complete the journey. Everything that had to be said about the infrastructure was told and I only have to show you the internal workings of the blocking collection itself.

[Step 1: Three steps to the blocking collection: [1] Inverse semaphore]

[Step 2: Dynamic lock-free queue – doing it right]

The blocking collecting is exposed as an interface that lives in the OtlCollections unit.

  IOmniBlockingCollection = interface(IGpTraceable) 
procedure Add(const value: TOmniValue);
procedure CompleteAdding;
function GetEnumerator: IOmniValueEnumerator;
function IsCompleted: boolean;
function Take(var value: TOmniValue): boolean;
function TryAdd(const value: TOmniValue): boolean;
function TryTake(var value: TOmniValue; timeout_ms: cardinal = 0): boolean;
end; { IOmniBlockingCollection }

There’s also a class TOmniBlockingCollection which implements this interface. This class is public and can be used or reused in your code.

The blocking collection works in the following way:

  • Add will add new value to the collection (which is internally implemented as a queue (FIFO, first in, first out)).
  • CompleteAdding tells the collection that all data is in the queue. From now on, calling Add will raise an exception.
  • TryAdd is the same as Add except that it doesn’t raise an exception but returns False if the value can’t be added.
  • IsCompleted returns True after the CompleteAdding has been called.
  • Take reads next value from the collection. If there’s no data in the collection, Take will block until the next value is available. If, however, any other thread calls CompleteAdding while the Take is blocked, Take will unblock and return False.
  • TryTake is the same as Take except that it has a timeout parameter specifying maximum time the call is allowed to wait for the next value.
  • Enumerator calls Take in the MoveNext method and returns that value. Enumerator will therefore block when there is no data in the collection. The usual way to stop the enumerator is to call CompleteAdding which will unblock all pending MoveNext calls and stop enumeration. [For another approach see the example at the end of this article.]

The trivial parts

Most of the blocking collection code is fairly trivial.

Add just calls TryAdd and raises an exception if TryAdd fails.

procedure TOmniBlockingCollection.Add(const value: TOmniValue);
if not TryAdd(value) then
raise ECollectionCompleted.Create('Adding to completed collection');
end; { TOmniBlockingCollection.Add }

CompleteAdding sets two “completed” flags – one boolean flag and one Windows event. Former is used for speed in non-blocking tests while the latter is used when TryTake has to block.

procedure TOmniBlockingCollection.CompleteAdding;
if not obcCompleted then begin
obcCompleted := true;
end; { TOmniBlockingCollection.CompleteAdding }

Take calls the TryTake with the INFINITE timeout.

function TOmniBlockingCollection.Take(var value: TOmniValue): boolean;
Result := TryTake(value, INFINITE);
end; { TOmniBlockingCollection.Take }

TryAdd checks if CompleteAdding has been called. If not, the value is stored in the dynamic queue.

There’s a potential problem hiding in the TryAdd – between the time the completed flag is checked and the time the value is enqueued, another thread may call CompleteAdding. Strictly speaking, TryAdd should not succeed in that case. However, I cannot foresee a parallel algorithm where this could cause a problem.

function TOmniBlockingCollection.TryAdd(const value: TOmniValue): boolean;
// CompleteAdding and TryAdd are not synchronised
Result := not obcCompleted;
if Result then
end; { TOmniBlockingCollection.TryAdd }

Easy peasy.

The not so trivial part

And now for something completely different …

TryTake is a whole different beast. It must:

  • retrieve the data
  • observe IsCompleted
  • block when there’s no data and observer is completed
  • observe the timeout limitations

Not so easy.

In addition to the obcCompletedSignal (completed event) and obcCollection (dynamic data queue) it will also use obcObserver (a queue change mechanism used inside the OTL) and obcResourceCount, which is an instance of the TOmniResourceCount (inverse semaphore, introduced in Part 1). All these are created in the constructor:

constructor TOmniBlockingCollection.Create(numProducersConsumers: integer);
inherited Create;
if numProducersConsumers > 0 then
obcResourceCount := TOmniResourceCount.Create(numProducersConsumers);
obcCollection := TOmniQueue.Create;
obcCompletedSignal := CreateEvent(nil, true, false, nil);
obcObserver := CreateContainerWindowsEventObserver;
obcSingleThreaded := (Environment.Process.Affinity.Count = 1);
if obcSingleThreaded then
obcCollection.ContainerSubject.Attach(obcObserver, coiNotifyOnAllInserts);
end; { TOmniBlockingCollection.Create }

TryTake is pretty long so I’ve split it into two parts. Let’s take a look at the non-blocking part first.

First, the code tries to retrieve data from the dynamic queue. If there’s data available, it is returned. End of story.

Otherwise, the completed flag is checked. If CompleteAdding has been called, TryTake returns immediately. It also returns if timeout is 0.

Otherwise, the code prepares for the blocking wait. Resource counter is allocated (reasons for this will be provided later), and observer is attached to the blocking collection. This observer will wake the blocking code when new value is stored in the collection.

[In the code below you can see a small optimization – if the code is running on a single core then the observer is attached in the TOmniBlockingCollection constructor and detached in the destructor. Before this optimization was introduced, Attach and Detach spent much too much time in busy-wait code (on a single-core computer).]

After all that is set, the code waits for the value (see the next code block), observer is detached from the queue and resource counter is released.

function TOmniBlockingCollection.TryTake(var value: TOmniValue;
timeout_ms: cardinal): boolean;
awaited : DWORD;
startTime : int64;
waitHandles: array [0..2] of THandle;
if obcCollection.TryDequeue(value) then
Result := true
else if IsCompleted or (timeout_ms = 0) then
Result := false
else begin
if assigned(obcResourceCount) then
if not obcSingleThreaded then
obcCollection.ContainerSubject.Attach(obcObserver, coiNotifyOnAllInserts);
//wait for the value, see the next code block below
if not obcSingleThreaded then
obcCollection.ContainerSubject.Detach(obcObserver, coiNotifyOnAllInserts);
if assigned(obcResourceCount) then
end; { TOmniBlockingCollection.TryTake }

Blocking part starts by storing the current time (millisecond-accurate TimeGetTime is used) and preparing wait handles. Then it enters the loop which repeats until the CompleteAdding has been called or timeout has elapsed (the Elapsed function which I’m not showing here for the sake of simplicty; see the source) or a value was dequeued.

In the loop, the code tries again to dequeue a value from the dynamic queue and exits the loop if dequeue succeeds. Otherwise, a WaitForMultipleObjects is called. This wait waits for one of three conditions:

  • Completed event. If this event is signalled, CompleteAdding has been called and TryTake must exit.
  • Observer event. If this event is signalled, new value was enqueued into the dynamic queue and code must try to dequeue this value.
  • Resource count event. If this event is signalled, all resources are used and the code must exit (more on that later).
        startTime := DSiTimeGetTime64;
waitHandles[0] := obcCompletedSignal;
waitHandles[1] := obcObserver.GetEvent;
if assigned(obcResourceCount) then
waitHandles[2] := obcResourceCount.Handle;
Result := false;
while not (IsCompleted or Elapsed) do begin
if obcCollection.TryDequeue(value) then begin
Result := true;
break; //while
awaited := WaitForMultipleObjects(IFF(assigned(obcResourceCount), 3, 2),
@waitHandles, false, TimeLeft_ms);
if awaited <> WAIT_OBJECT_1 then begin
if awaited = WAIT_OBJECT_2 then
Result := false;
break; //while

If new value was enqueued into the dynamic queue, TryDequeue is called again. It is entirely possible that another thread calls that function first and removes the value causing TryDequeue to fail and WaitForMultipleObjects to be called again. Such is life in the multithreaded world.

Enumerating the blocking collection

TOmniBlockingCollection enumerator is slightly more powerful than the usual Delphi enumerator. In addition to the usual methods it contains function Take which is required by the Parallel architecture (see Parallel.For and Parallel.ForEach.Aggregate for more information).

  IOmniValueEnumerator = interface ['{F60EBBD8-2F87-4ACD-A014-452F296F4699}']
function GetCurrent: TOmniValue;
function MoveNext: boolean;
function Take(var value: TOmniValue): boolean;
property Current: TOmniValue read GetCurrent;
end; { IOmniValueEnumerator }
  TOmniBlockingCollectionEnumerator = class(TInterfacedObject,
constructor Create(collection: TOmniBlockingCollection);
function GetCurrent: TOmniValue; inline;
function MoveNext: boolean; inline;
function Take(var value: TOmniValue): boolean;
property Current: TOmniValue read GetCurrent;
end; { TOmniBlockingCollectionEnumerator }

The implementation is trivial.

constructor TOmniBlockingCollectionEnumerator.Create(collection: TOmniBlockingCollection);
obceCollection_ref := collection;
end; { TOmniBlockingCollectionEnumerator.Create }

function TOmniBlockingCollectionEnumerator.GetCurrent: TOmniValue;
Result := obceValue;
end; { TOmniBlockingCollectionEnumerator.GetCurrent }

function TOmniBlockingCollectionEnumerator.MoveNext: boolean;
Result := obceCollection_ref.Take(obceValue);
end; { TOmniBlockingCollectionEnumerator.MoveNext }

function TOmniBlockingCollectionEnumerator.Take(var value: TOmniValue): boolean;
Result := MoveNext;
if Result then
value := obceValue;
end; { TOmniBlockingCollectionEnumerator.Take }


A not-so-simple how to on using the blocking collection can be seen in the demo 34_TreeScan. It uses the blocking collection to scan a tree with multiple parallel threads. This demo works in Delphi 2007 and newer.

A better example of using the blocking collection is in the demo 35_ParallelFor. Actually, it uses the same approach as demo 34 to scan the tree, except that the code is implemented as an anonymous method which causes it to be much simpler than the D2007 version. Of course, this demo works only in Delphi 2009 and above.

This is the full parallel scanner from the 35_ParallelFor demo:

function TfrmParallelForDemo.ParaScan(rootNode: TNode; value: integer): TNode;
cancelToken: IOmniCancellationToken;
nodeQueue : IOmniBlockingCollection;
nodeResult : TNode;
numTasks : integer;
nodeResult := nil;
cancelToken := CreateOmniCancellationToken;
numTasks := Environment.Process.Affinity.Count;
nodeQueue := TOmniBlockingCollection.Create(numTasks);
Parallel.ForEach(nodeQueue as IOmniValueEnumerable)
.NumTasks(numTasks) // must be same number of task as in
nodeQueue to ensure stopping

procedure (const elem: TOmniValue)
childNode: TNode;
node : TNode;
node := TNode(elem.AsObject);
if node.Value = value then begin
nodeResult := node;
else for childNode in node.Children do
Result := nodeResult;
end; { TfrmParallelForDemo.ParaScan }

The code first creates a cancellation token which will be used to stop the Parallel.ForEach loop. Number of tasks is set to number of cores accessible from the process and a blocking collection is created. Resource count for this collection is initialized to the number of tasks (parameter to the TOmniBlockingCollection.Create). The root node of the tree is added to the blocking collection.

Then the Parallel.ForEach is called. The IOmniValueEnumerable aspect of the blocking collection is passed to the ForEach. Currently, this is the only way to provide ForEach with data. This interface just tells the ForEach how to generate enumerator for each worker thread. [At the moment, each worker requires a separate enumerator. This may change in the future.]

  IOmniValueEnumerable = interface ['{50C1C176-C61F-41F5-AA0B-6FD215E5159F}']
function GetEnumerator: IOmniValueEnumerator;
end; { IOmniValueEnumerable }

The code also passes cancellation token to the ForEach loop and starts the parallel execution (call to Execute). In each parallel task, the following code is executed (this code is copied from the full ParaScan example above):

      procedure (const elem: TOmniValue)
childNode: TNode;
node : TNode;
node := TNode(elem.AsObject);
if node.Value = value then begin
nodeResult := node;
else for childNode in node.Children do

The code is provided with one element from the blocking collection (ForEach takes care of that). If the Value field is the value we’re searching for, nodeResult is set, blocking collection is put into CompleteAdding state (so that enumerators in other tasks will terminate blocking wait (if any)) and ForEach is cancelled.

Otherwise (not the value we’re looking for), all the children of the current node are added to the blocking collection. TryAdd is used (and its return value ignored) because another thread may call CompleteAdding while the for childNode loop is being executed.

That’s all! There is a blocking collection into which nodes are put (via the for childNode loop) and from which they are removed (via the ForEach infrastructure). If child nodes are not provided fast enough, blocking collection will block on Take and one or more tasks may sleep for some time until new values appear. Only when the value is found, the blocking collection and ForEach loop are completed/cancelled.

This is very similar to the code that was my inspiration for writing the blocking collection:

var targetNode = …;
var bc = new BlockingCollection<Node>(startingNodes);
// since we expect GetConsumingEnumerable to block, limit parallelism to the number of
// procs, avoiding too much thread injection
var parOpts = new ParallelOptions() { MaxDegreeOfParallelism = Enivronment.ProcessorCount };
Parallel.ForEach(bc.GetConsumingEnumerable(), parOpts, (node,loop) =>
    if (node == targetNode)
        foreach(var neighbor in node.Neighbors) bc.Add(neighbor);

However, this C# code exhibits a small problem. If the value is not to be found in the tree, the code never stops. Why? All tasks eventually block in the Take method (because complete tree has been scanned) and nobody calls CompleteAdding and loop.Stop. Does the Delphi code contains the very same problem?

Definitely not! That’s exactly why the resource counter was added to the blocking collection!

If the blocking collection is initialized with number of resources greater then zero, it will allocate a resource counter in the constructor. This resource counter is allocated just before the thread blocks in TryTake and released after that. Each blocking wait in TryTake waits for this resource counter to become signalled. If all threads try to execute blocking wait, this resource counter drops to zero, signals itself and unblocks all TryTake calls!

This elegant solution has only one problem – resource counter must be initialized to the number of threads that will be reading from the blocking collection. That’s why in the code above (ParaScan) same number is passed to the blocking collection constructor (resource counter initialization) and to the ForEach.NumTasks method (number of parallel threads).


TOmniBlockingCollection will be available in the OmniThreadLibrary 1.05, which will be released in few days.

For the impatient there is OTL 1.05 Release Candidate. The only code that will change between 1.05 RC and release are possible bug fixes.

OmniThreadLibrary 1.05 Release Candidate

Next OTL release is coming soon. Brave souls are invited to download and test 1.05 RC. If you find any problem, make sure to report it in the OmniThreadLibrary forum or here in comments.
1.05RC tag in SVN
list of changes

Thursday, February 18, 2010

Dynamic lock-free queue – doing it right

Some history required …
First there was a good idea with somewhat patchy implementation: Three steps to the blocking collection: [2] Dynamically allocated queue.
Then there was a partial solution, depending on me being able to solve another problem. Still, it was a good solution: Releasing queue memory without the MREW lock.
At the end, the final (actually, the original) problem was also solved: Bypassing the ABA problem.
And now to the results …

This article describes a lock-free, (nearly) O(1) insert/remove, dynamically allocated queue that doesn’t require garbage collector. It can be implemented on any hardware that supports 8-byte compare-and-swap operation (in Intel world, that means at least a Pentium). The code uses 8-byte atomic move in some parts but they can be easily changed into 8-byte CAS in case the platform doesn’t support such operation. In the current implementation, Move64 (8-byte move) function uses SSE2 instructions and therefore requires Pentium 4. The code, however, can be conditionally compiled with CAS64 instead of Move64 thus enabling it to run on Pentium 1 to 3. (See the notes in the code for more information). The code requires memory manager that allows the memory to be released in a thread different from the thread where allocation occurred. [Obviously, Windows on Intel platform satisfies all conditions.]
Although the dynamic queue has been designed with the OmniThreadLibrary (OTL for short) in mind, there’s also a small sample implementation that doesn’t depend on the OTL: GpLockFreeQueue.pas. This implementation can store int64 elements only (or everything you can cast into 8 bytes) while the OTL implementation from OtlContainers stores TOmniValue data. [The latter being a kind of variant record used inside the OTL to store “anything” from a byte to a string/wide string/object/interface.] Because of that, GpLockFreeQueue implementation is smaller, faster, but slightly more limited. Both are released under the BSD license.

Memory layout

Data is stored in slots. Each slot uses 16 bytes and contains byte-size tag, word-size offset and up to 13 bytes of data. The implementation in OtlContainers uses all of those 13 bytes to store TOmniValue while the implementation in GpLockFreeQueue uses only 8 bytes and keeps the rest unused.
The following notation is used to represent a slot: [tag|offset|value].
In reality, value field is first in the record because it must be 4-aligned. The reason for that will be revealed in a moment. In GpLockFreeQueue, a slot is defined as:
  TGpLFQueueTaggedValue = packed record
    Value   : int64;
    Tag     : TGpLFQueueTag;
    Offset  : word;
    Stuffing: array [1..5] of byte;
  end; { TGpLFQueueTaggedValue }
Slots do not stand by themselves; they are allocated in blocks. Default block size if 64 KB (4096 slots) but can be varied from 64 bytes (four slots) to 1 MB (65536 slots). In this article, I’ll be using 5-slot blocks, as they are big enough to demonstrate all the nooks and crannies of the algorithm and small enough to fit in one line of text.
During the allocation, each block is formatted as follows:
[Header|0|4] [Sentinel|1|0] [Free|2|0] [Free|3|0] [EndOfList|4|0]
The first slot is marked as a Header and has the value field initialized to “number of slots in the block minus one”. [The highest value that can be stored in the header’s value field is 65535; therefore the maximum number of slots in a block is 65536.] This value is atomically decremented each time a slot is dequeued. When the number drops to zero, block can be released. (More on that in: Releasing queue memory without the MREW lock.) InterlockedDecrement, which is used to decrement this value, requires its argument to be 4-aligned and that’s the reason for the value field to be stored first in the slot.
The second slot is a Sentinel. Slots from the third onwards are tagged Free and are used to store data. The last slot is tagged EndOfList and is used to link two blocks. All slots have the offset field initialized to the sequence number of the slot – in the Header this value is 0, in the Sentinel 1, and so on up to the EnndOfList with the value set to 4 (number of slots in the block minus 1). This value is used in the Dequeue to calculate the address of the header slot just before the header’s value is decremented.
In addition to dynamically allocated (and released) memory blocks, the queue uses head and tail tagged pointers. Both are 8-byte values, consisting of two 4-byte fields – slot and tag. The following notation is used to represent a tagged pointer: [slot|tag].
The slot field contains the address of the current head/tail slot while the tag field contains the tag of the current slot. The motivation behind this scheme is explained in the Bypassing the ABA problem post.
Tail and head pointers are modified using 8-byte CAS and Move commands and must therefore be 8-aligned.
By putting all that together, we get a snapshot of the queue state. This is the initial state of a queue with five-slot blocks:
B1:[Header|0|4] T:[Sentinel|1|0] H:[Free|2|0] [Free|3|0] [EndOfList|4|0]
The memory block begins at address B1 and contains five slots, initialized as described before. The tail pointer points to the second slot of block B1 (B1:1; I’m using the form address:offset), which is tagged Sentinel and the head pointer points to the third block (B1:2), the first Free slot. Here we see the sole reason for the Sentinel – it stands between the tail and the head when the queue is empty.


In theory, the enqueue operation is simple. The element is stored in the next available slot and queue head is advanced. In practice, however, multithreading makes things much more complicated.
To prevent thread conflicts, each enqueueing thread must first take ownership of the head. It does this by swapping queue head tag from Free to Allocating or from EndOfList to Extending. To prevent ABA problems, both head pointer and head tag are swapped with the same head pointer and new tag in one atomic 8-byte compare-and-swap.
Enqueue then does its work and at the end swaps (head pointer, tag) to (next head pointer, Free|EndOfList) which allows other threads to proceed with their enqueue operation.
Let’s start with the empty list.
B1:[Header|0|4] T:[Sentinel|1|0] H:[Free|2|0] [Free|3|0] [EndOfList|4|0]
Enqueue first swaps [B1:2|Free] with [B1:2|Allocating].
B1:[Header|0|4] T:[Sentinel|1|0] H:[Free|2|0] [Free|3|0] [EndOfList|4|0]
The green colour indicates an atomic change.
Only the head tag has changed, the data in the B1 memory block is not modified. Head still points to a slot tagged Free (slot B1:2). This is fine as enqueueing threads don’t take interest in this tag at all.
Data is then stored in the slot and its tag is changed to Allocated. This again makes no change to enqueuers as the head slot in the header was not updated yet. It also doesn’t allow the dequeue operation on this slot to proceed because the head is adjacent to the tail, which points to a Sentinel and in this case Dequeue treats the queue as empty (as we’ll see later).
B1:[Header|0|4] T:[Sentinel|1|0] H:[Allocated|2|42] [Free|3|0] [EndOfList|4|0]
Red colour marks “unsafe” modification.
At the end, the head is unlocked by storing address of the next slot (first free slot, B1:3) and next slot’s tag (Free).
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] H:[Free|3|0] [EndOfList|4|0]
Teal colour marks an atomic 8-byte move used to move new data into the head pointer. If the target platform doesn’t support such move, an 8-byte CAS could be used instead.
After those changes, head is pointing to the next free slot and data is stored in the queue.
Let’s assume that another Enqueue is called and stores number 17 in the queue. Nothing new happens here.
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
The next Enqueue must do something new as there are no free slots in the current block. To extend the queue, thread first swaps the EndOfList tag with the Extending tag. By doing this, the thread takes ownership of the queue head.
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
A new block gets allocated and initialized (see chapter on memory management, below).
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
B2:[Header|0|4] [Sentinel|1|0] [Free|2|0] [Free|3|0] [EndOfList|4|0]
Data is stored in the first free slot of the block B2.
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
B2:[Header|0|4] [Sentinel|1|0] [Allocated|2|57] [Free|3|0] [EndOfList|4|0]
Last slot of block B1 is modified to point to the first element in the second slot of the next block (Sentinel). Also, a tag BlockPointer is stored into that slot.
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] H:[BlockPointer|4|B2:1]
B2:[Header|0|4] [Sentinel|1|0] [Allocated|2|57] [Free|3|0] [EndOfList|4|0]
At the end, the head is updated to point to the first free slot (B2:3).
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] [BlockPointer|4|B2:1]
B2:[Header|0|4] [Sentinel|1|0] [Allocated|2|57] H:[Free|3|0] [EndOfList|4|0]
That completes the Enqueue. List head is now unlocked.
The actual code is not more complicated than this description (code taken from GpLockFreeQueue).
procedure TGpLockFreeQueue.Enqueue(const value: int64);
  extension: PGpLFQueueTaggedValue;
  next     : PGpLFQueueTaggedValue;
  head     : PGpLFQueueTaggedValue;
    head := obcHeadPointer.Slot;
    if (obcHeadPointer.Tag = tagFree)
       and CAS64(head, Ord(tagFree), head, Ord(tagAllocating), obcHeadPointer^)
      break //repeat
    else if (obcHeadPointer.Tag = tagEndOfList)
            and CAS64(head, Ord(tagEndOfList), head, Ord(tagExtending), obcHeadPointer^)
      break //repeat
    else  // very temporary condition, retry quickly
      asm pause; end;
  until false;
  if obcHeadPointer.Tag = tagAllocating then begin // enqueueing
    next := NextSlot(head);
    head.Value := value;
    head.Tag := tagAllocated;
    Move64(next, Ord(next.Tag), obcHeadPointer^); // release the lock
  else begin // allocating memory
    extension := AllocateBlock; // returns pointer to the header
    Inc(extension, 2);          // move over header and sentinel to the first data slot
    extension.Tag := tagAllocated;
    extension.Value := value;
    Dec(extension);             // forward reference points to the sentinel
    head.Value := int64(extension);
    head.Tag := tagBlockPointer;
    Inc(extension, 2); // get to the first free slot
    Move64(extension, Ord(extension.Tag), obcHeadPointer^); // release the lock
    PreallocateMemory; // preallocate memory block
end; { TGpLockFreeQueue.Enqueue }


Enqueue is simple but Dequeue is a whole new bag of problems. It has to handle the Sentinel slot and because of that there are five possible scenarios:
  1. Skip the sentinel.
  2. Read the data (tail doesn’t catch the head).
  3. Read the data (tail does catch the head).
  4. The queue is empty.
  5. Follow the BlockPointer tag.
To prevent thread conflicts, dequeueing thread takes ownership of the tail. It does this by swapping the tail tag from Allocated or Sentinel to Removing or from BlockPointer to Destroying. Again, those changes are done atomically by swapping both tail pointer and tail tag in one go.
Let’s walk through all five scenarios now.
1 – Skip the sentinel
Let’s start with a queue state where two slots are allocated and head points to the EndOfList slot.
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
The code first locks the tail.
B1:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
As there is no data in the Sentinel slot, the tail is immediately updated to point to the next slot.
B1:[Header|0|4] [Sentinel|1|0] T:[Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
There’s no need to update the tag in slot 1 as no other thread can reach it again. Because the slot is now unreachable, the code now decrements the count in the B1’s Header slot (from 4 to 3).
B1:[Header|0|3] [Sentinel|1|0] T:[Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
Because the original tag was Sentinel, the code retries from beginning immediately. The queue is now in scenario 2 (data, the tail is not immediately before the head).
2 - Read the data (tail doesn’t catch the head)
Again, the tail is locked.
B1:[Header|0|3] [Sentinel|1|0] T:[Allocated|2|42] [Allocated|3|17] H:[EndOfList|4|0]
The code then reads the value from the slot (42) and advances the tail to the slot B1:3.
B1:[Header|0|3] [Sentinel|1|0] [Allocated|2|42] T:[Allocated|3|17] H:[EndOfList|4|0]
Again, there is no need to change the slot tag. The slot 2 is now unreachable and the Header count is decremented.
B1:[Header|0|2] [Sentinel|1|0] [Allocated|2|42] T:[Allocated|3|17] H:[EndOfList|4|0]
The code has retrieved the data and can now return from the Dequeue method.
3 - Read the data (tail does catch the head)
If the Dequeue is now called for the second time, we have the scenario 3 – there is data in the queue, but the head pointer is next to the tail pointer. Because of the, the tail cannot be incremented. Instead of that, the code replaces the tail slot tag with the Sentinel.
It is entirely possible that the head will change the very next moment which means that the Sentinel would not be really needed. Luckily, that doesn’t hurt much – the next Dequeue would skip the Sentinel, retry and fetch the next element from the queue.
The code starts in a well-known manner, by taking ownership of the tail.
B1:[Header|0|2] [Sentinel|1|0] [Allocated|2|42] T:[Allocated|3|17] H:[EndOfList|4|0]
The code then reads the value from the slot, but because the head was next to tail when Dequeue was called, the code doesn’t increment the tail and doesn’t decrement the Header counter. Instead of that, the Sentinel tag is put into the head tag.
B1:[Header|0|2] [Sentinel|1|0] [Allocated|2|42] T:[Allocated|3|17] H:[EndOfList|4|0]
It doesn’t matter that the slot tag is still Allocated as no-one will read it again.
4 - The queue is empty
If the Dequeue would be called now, it would return immediately with status empty because the tail tag is Sentinel and because the tail has caught the head.
5 - Follow the BlockPointer tag
In the last scenario, the tail is pointing to a BlockPointer.
B1:[Header|0|1] [Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] T:[BlockPointer|4|B2:1]
B2:[Header|0|4] [Sentinel|1|0] [Allocated|2|57] H:[Free|3|0] [EndOfList|4|0]
As expected, the code first takes the ownership of the tail.
B1:[Header|0|1] [Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] T:[BlockPointer|4|B2:1]
B2:[Header|0|4] [Sentinel|1|0] [Allocated|2|57] H:[Free|3|0] [EndOfList|4|0]
We know that the first slot in the next block is Sentinel. We also know that the head is not pointing to this slot because that’s how Enqueue works (when new block is allocated, head points to the first slot after the Sentinel.). Therefore, it is safe to update the tail to point to the Sentinel slot of the B2 block.
B1:[Header|0|1] [Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] [BlockPointer|4|B2:1]
B2:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|57] H:[Free|3|0] [EndOfList|4|0]
By doing the swap, the ownership of the tail is released.
The Header count is then decremented.
B1:[Header|0|0] [Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] [BlockPointer|4|B2:1]
B2:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|57] H:[Free|3|0] [EndOfList|4|0]
Because the count is now zero, the code destroys the B1 block. (Note that the Header count decrement is atomic and only one thread can actually reach the zero.) While the block is being destroyed, other threads may be calling Dequeue.
B1:[Header|0|0] [Sentinel|1|0] [Allocated|2|42] [Allocated|3|17] [BlockPointer|4|B2:1] B2:[Header|0|4] T:[Sentinel|1|0] [Allocated|2|57] H:[Free|3|0] [EndOfList|4|0]
Because the tail tag was originally BlockPointer, the code retries immediately and continues with the scenario 1.
The actual code is tricky because some of the code path is shared between scenarios (code taken from GpLockFreeQueue).
function TGpLockFreeQueue.Dequeue(var value: int64): boolean;
  caughtTheHead: boolean;
  tail         : PGpLFQueueTaggedValue;
  header       : PGpLFQueueTaggedValue;
  next         : PGpLFQueueTaggedValue;
  tag          : TGpLFQueueTag;
  tag := tagSentinel;
  Result := true;
  while Result and (tag = tagSentinel) do begin
      tail := obcTailPointer.Slot;
      caughtTheHead := NextSlot(obcTailPointer.Slot) = obcHeadPointer.Slot; 
      if (obcTailPointer.Tag = tagAllocated)
         and CAS64(tail, Ord(tagAllocated), tail, Ord(tagRemoving), obcTailPointer^) then
        tag := tagAllocated;
        break; //repeat
      else if (obcTailPointer.Tag = tagSentinel) then begin
        if caughtTheHead then begin
          Result := false;
          break; //repeat
        else if CAS64(tail, Ord(tagSentinel), tail, Ord(tagRemoving), obcTailPointer^) then begin
          tag := tagSentinel;
          break; //repeat
      else if (obcTailPointer.Tag = tagBlockPointer)
              and CAS64(tail, Ord(tagBlockPointer), tail, Ord(tagDestroying), obcTailPointer^) then
        tag := tagBlockPointer;
        break; //repeat
        asm pause; end;
    until false;
    if Result then begin // dequeueing
      header := tail;
      Dec(header, header.Offset);
      if tag in [tagSentinel, tagAllocated] then begin
        next := NextSlot(tail);
        if tag = tagAllocated then // sentinel doesn't contain any useful value
          value := tail.Value;
        if caughtTheHead then begin  // release the lock; as this is the last element, don't move forward
          Move64(tail, Ord(tagSentinel), obcTailPointer^);
          header := nil; // do NOT decrement the counter; this slot will be retagged again
          Move64(next, Ord(next.Tag), obcTailPointer^); // release the lock
      else begin // releasing memory
        next := PGpLFQueueTaggedValue(tail.Value); // next points to the sentinel
        Move64(next, Ord(tagSentinel), obcTailPointer^); // release the lock
        tag := tagSentinel; // retry
      if assigned(header) and (InterlockedDecrement(PInteger(header)^) = 0) then
  end; //while Result and (tag = tagSentinel)
end; { TGpLockFreeQueue.Dequeue }

Memory management

In the dynamic queue described above, special consideration goes to memory allocation and deallocation because most of the time that will be the slowest part of the enqueue/dequeue.
Memory is always released after the queue tail is unlocked. That way, other threads may dequeue from the same queue while the thread is releasing the memory.
The allocation is trickier, because the Enqueue only knows that it will need the memory after the head is locked. The trick here is to use one preallocated memory block which is reused inside the Enqueue. This is much faster than calling the allocator. After the head is unlocked, Enqueue preallocates next block of memory. This will slow down the current thread, but will not block other threads from enqueueing into the same queue.
Dequeue also tries to help with that. If the preallocated block is not present when a block must be released, Dequeue will store the released block away for the next Enqueue to use.
Also, there's one such block preallocated when the queue is initially created.
If this explanation is unclear, look at the program flow below. It describes the code flow through the Enqueue that has to allocate a memory block and through the Dequeue that has to release a memory block. Identifiers in parenthesis represent methods listed below.
  • lock the head
  • detect EndOfList
  • use the cached block if available, otherwise allocate a new block (AllocateBlock)
  • unlock the head
  • if there is no cached block, allocate new block and store it away (PreallocateMemory)
  • lock the tail
  • process last slot in the block
  • unlock the tail
  • decrement the header count
  • as the header count has dropped to zero:
    • if the cached block is empty, store this one away (ReleaseBlock)
    • otherwise release the block
All manipulations with the cached block are done atomically. All allocations are optimistic – if the preallocated block is empty, new memory block is allocated, partitioned and only then the code tries to swap it into the preallocated block variable. If compare-and-swap fails at this point, other thread went through the same routine, just slightly faster, and the allocated (and partitioned) block is thrown away. Looks like there may be quite some work done in vain but in reality the preallocated block is rarely thrown away.
It tested other, more complicated schemes (for example small 4-slot stack) but they invariably behaved worse than this simple approach.
function TGpLockFreeQueue.AllocateBlock: PGpLFQueueTaggedValue;
  cached: PGpLFQueueTaggedValue;
  cached := obcCachedBlock;
  if assigned(cached) and CAS32(cached, nil, obcCachedBlock) then
    Result := cached
  else begin
    Result := AllocMem(obcBlockSize);
end; { TGpLockFreeQueue.AllocateBlock }

procedure TGpLockFreeQueue.PreallocateMemory;
  memory: PGpLFQueueTaggedValue;
  if not assigned(obcCachedBlock) then begin
    memory := AllocMem(obcBlockSize);
    if not CAS32(nil, memory, obcCachedBlock) then
end; { TGpLockFreeQueue.PreallocateMemory }
procedure TGpLockFreeQueue.ReleaseBlock(firstSlot: PGpLFQueueTaggedValue; forceFree: boolean);
  if forceFree or assigned(obcCachedBlock) then
  else begin
    ZeroMemory(firstSlot, obcBlockSize);
    if not CAS32(nil, firstSlot, obcCachedBlock) then
end; { TGpLockFreeQueue.ReleaseBlock }
As you can see in the code fragments above, memory is also initialized (formatted into slots) when memory is allocated. This also helps with the general performance.


Tests were again performed using the 32_Queue project in the Tests branch of the OTL tree.
The test framework sets up the following data path:
source queue –> N threads –> channel queue –> M threads –> destination queue
Source queue is filled with numbers from 1 to 1.000.000. Then 1 to 8 threads are set up to read from the source queue and write into the channel queue and another 1 to 8 threads are set up to read from the channel queue and write to the destination queue. Application then starts the clock and starts all threads. When all numbers are moved to the destination queue, clock is stopped and contents of the destination queue are verified. Thread creation time is not included in the measured time.
All in all this results in 2 million reads and 2 million writes distributed over three queues. Tests are very brutal as all threads are just hammering on the queues, doing nothing else. The table below contains average, min and max time of 5 runs on a 2.67 GHz computer with two 4-core CPUs. Data from the current implementation ("new code") is compared to the original implementation ("old code"). Best times are marked green.
New code
average [min-max] all data in milliseconds millions of queue operations per second
N = 1, M = 1 590 [559 – 682] 6.78
N = 2, M = 2 838 [758 – 910] 4.77
N = 3, M = 3 1095 [1054 – 1173] 3.65
N = 4, M = 4 1439 [1294 – 1535] 2.78
N = 8, M = 8 1674 [1303 – 2217] 2.39
N = 1, M = 7 1619 [1528 – 1822] 2.47
N = 7, M = 1 1525 [1262 – 1724] 2.62
Old Code
average [min-max]all data in milliseconds millions of queue operations per second
N = 1, M = 1 707 [566-834] 5.66
N = 2, M = 2 996 [950-1031] 4.02
N = 3, M = 3 1065 [1055-1074] 3.76
N = 4, M = 4 1313 [1247-1358] 3.04
N = 8, M = 8 1520 [1482-1574] 2.63
N = 1, M = 7 3880 [3559-4152] 1.03
N = 7, M = 1 1314 [1299-1358] 3.04

The new implementation is faster when less threads are used and slightly slower when number of threads increases. The best thing is that there is no weird speed drop in N = 1, M = 7 case. The small slowdown with higher number of threads doesn't bother me much as this test case really stresses the queue. In all practical applications, there should be much more code that does real work and queue load would rapidly drop down.
If your code depends on accessing a shared queue from many multiple threads that enqueue/dequeue most of the time, there's a simple solution - change the code! I believe that multithreaded code should not fight for each data, but cooperate. A possible solution is to split the data in packets and schedule packets to the shared queue. Each thread would then dequeue one packet and process all data stored within.


The code will be released in OmniThreadLibrary 1.5 (but you can use it already if you fetch the HEAD from the SVN). It passed very rigorous stress test and I believe it is working. If you find any problems, please let me know. I’m also interested in any ports to different languages (a C version would be nice).

Wednesday, February 10, 2010

Bypassing the ABA problem

On Saturday me and my wife visited a classical music concert. Although I like this kind of music, the particular combination of instruments (harp and violin) didn’t really interest me that much, especially when they played modernist Slovenian composers. [Debussy, on the other hand, was superb.]

Anyway, I got submerged into music and half of my brain switched of and then I got all sorts of weird programming ideas. The first was how to solve the ABA problem in the initial dynamic queue implementation. [Total failure, that idea, it didn’t work at all.] The second, however, proved to be very useful as it solved the memory release problem (provided that ABA gets fixed, of course).

The new memory release scheme brought with it a new strength of will. If I had solved that one, then maybe, just maybe, I can also solve the ABA problem, I thought to myself and returned to the code. And then it dawned on me …

The problem with the initial implementation was that the head/tail pointer and corresponding tag were access asynchronously. In the multithreading environment, that is always a problem. Somehow I had to modify them at the same time, but that didn’t look feasible as the tag was living in the dynamically allocated block and the head/tail pointer was stored in the object itself. I couldn’t put the head/tail into the block, but I could put a tag near to the head/tail pointer! [A copy of the tag, actually, as I still needed the tags to be stored in the data block.] Then I could use 8-byte compare-and-swap to change both the pointer and the tag at the same time!

There was one problem, though. In the initial implementation, the tail was allowed to catch the head. If that happened with the new scheme, both head and tail pointers would be the same (and pointing to a tagFree slot) but the first enqueue operation would only modify the head tag, although the tail tag would in reality also change! It seemed like I was just pushing the ABA problem from place to place :(

Still, there is a simple (at least for some values of that word) solution to such problems – introduce the sentinel. This is a special element signifying that some pointer (tail, in my case) has reached the end of list. A good idea, but could it be made to work?

I fired up my trusty spreadsheed (very good stuff for simulations) and in few hours I had the basic plan laid out.


[Yes, that’s the picture from the yesterday’s teaser.]

It was a really simple work to convert this to the code. After fixing few bugs, I had the new queue running, faster then ever before!

I’ll put together a long article describing all the tricks inside the new dynamic queue, but that will take some time, sorry. In the meantime, you can checkout the current OtlContainers and read the pseudo-code documentation.



header contains:
    slot = 4 bytes
    tag  = 4 bytes
    slot = 4 bytes
    tag  = 4 bytes
all are 4-aligned

slot contains:
  TOmniValue = 13 bytes
  tag        = 1 byte
  offset     = 2 bytes
TOmniValues are 4-aligned

block is initialized to:
[tagHeader, num slots - 1, 0] [tagSentinel, 0, 1] [tagFree 0, 2] .. [tagFree, 0, num slots - 2] [tagEndOfList, 0, num slots - 1]

      tail = header.tail.slot
      old_tag = header.tail.tag
      if header.tail.CAS(tail, tagFree, tail, tagAllocating) then
          tail.tag = tagAllocating
      else if header.tail.CAS(tail, tagEndOfList, tail, tagExtending) then
          tail.tag = tagExtending
  if old_tag = tagFree then
      store <value, tagAllocated> into slot
      header.tail.CAS(tail, tagAllocating, NextSlot(tail), NextSlot(tail).tag)
      allocate block // from cache, if possible
      next = second data slot in the new block
      set next to <tagAllocated, value>
      set last slot in the original block to <new block address, tagBlockPointer>
      header.tail.CAS(tail, tagExtending, next, next.tag)
      // queue is now unlocked
      preallocate block

      if header.head.tag = tagFree then
          return false
      head = header.head.slot
      old_tag = header.head.tag
      caughtTheTail := NextSlot(header.head.slot) = header.tail.slot;
      if head.head.CAS(head, tagAllocated, head, tagRemoving) then
          head.tag = tagRemovings
      else if header.head.Tag = tagSentinel then
          if caughtTheTail then
              return false
          else if header.head.CAS(head, tagSentinel, head, tagRemoving) then
              head.tag = tagRemoving
      else if header.head.CAS(head, tagBlockPointer, head, tagDestrogin) then
          head.tag = tagDestroying
  firstSlot = head - head.Offset // point to first slot
  if old_tag in [tagSentinel, tagAllocated] then
      next = NextSlot(head)
      if tag = tagAllocated then
          fetch stored value
      if caughtTheTail then
          header.head.CAS(head, tagRemoving, head, tagSentinel)
          firstSlot = nil // do not decrement the header counter
          header.head.CAS(head, tagRemoving, next, next.tag)
      next = head.value // points to the next block's sentinel
      header.head.CAS(head, tagDestroying, next, tagSentinel)
      old_tag = tagSentinel // force retry
  // queue is now unlocked
  if assigned(firstSlot) and (InterlockedDecrement(firstSlot.value) = 0) then
      release block
  if old_tag = tagSentinel
      retry from beginning