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Reimplementation and Performance Evaluation of a Lock-free Queue with Batching

This paper describes an implementation of a lock-free queue that utilizes batching to increase performance by up to 16x over standard lock-free queues. We attempted to implement the design and algorithms from the original research paper BQ: A Lock-Free Queue with Batching and tried to reproduce similar functionality and performance. The original implementation of this data structure was done in C++, but we wanted to try and build this in Java so we could compare the results of how the data structure performs in two different languages.

Our implementation of the lock-free queue provides all of the functionality available to a sequential queue. Our lock free batching extension builds upon the simple concurrent lock free queue implemented by Michael and Scott by vastly improving on scalability of the concurrent lock free queue. A concurrent queue has two access points of high contention, the head of the queue from which nodes are dequeued and the tail of the queue into where new nodes are enqueued. This inherently limits throughput of the data structure as only one thread can enqueue and one thread can dequeue at a time. Our version of a lock-free queue uses the idea of the future programming construct to perform batching operations as first seen in the paper written by Kogan and Herlithy [15]. Batching means to just group a sequence of standard operations to just one single batch operation, which then applies them together to the shared data structure.
Kogan and Herlithy originally proposed that operations with the same type (enqueue or dequeue) get added to the shared queue all at once. This means they execute each subsequence of enqueues together by appending them in order to the tail of the queue, and each subsequence of dequeues is removed from the head of the list all at the same time. The problem with this implementation is that performance can degrade if the operations in the batch frequently switch between enqueue and dequeue, as their implementation only allows for a batch to be built from sequential and like operations. The algorithm we implemented improves on this idea by batching these operations locally. Local batching allows our algorithm to handle both enqueue and dequeue operations in any order and build the result before applying it to the shared queue. This means it applies the batch operation all at once to the shared queue to reduce contention between multiple threads. This helps solve the problem that concurrent queues have with the contention of the head and tail pointers by batching these operations locally, which reduces the number of access attempts to the shared queue and improves scalability.
The Lock Free Queue with Batching designed by Gal Milman et al. [21] is an extension of the concurrent lock-free queue by Michael and Scott [20]. The Michael and Scott queue serves as the baseline for many developments on the concurrent FIFO queue, yet it is not scalable and only allows for one Enqueuer and one Dequeuer thread at a time. Moir et al. [22] presented a queue that increased scalability by allowing pairs of concurrent equeue and dequeue method calls to exchange values and eliminate themselves without having to access the shared queue under certain circumstances. This concept reduces contention upon the shared queue, a concept adopted and furthered by the Lock Free Queue with Batching as it locally pairs and eliminates multiple enqueue and dequeue operations before accessing the shared queue. The implementation of the Lock Free Queue with Batching most directly rises from the work done by Kogan and Herlihy which allows for batching of operations of the same type to be executed as a subsequence at once on the shared queue. The Lock Free Queue with Batching improves on the performance of the concurrent queue implemented by Kogan and Herlihy by allowing for alternation between enqueue and dequeue operations within a batch such as would most often be encountered in the general use case of a queue.

Overview
The Lock Free Queue with Batching is a direct extension of the basic concurrent Lock Free Queue. Our first step in building this data structure is to begin with a well-performing lock free queue implementation. The Lock Free Queue with Batching allows for quick computation of its size by maintaining counters with the head and tail of the shared queue. Batching threads will receive enqueue and dequeue operations to execute locally, building a subsequence of a queue that can be then applied to the shared queue atomically to perform multiple enqueue and dequeue operations in a single access to the shared queue. Local threads will also track the number of dequeue operations taking place to be able to quickly modify the shared queue as needed while applying the batch. When a batch is applied to the shared queue, a descriptor like object replaces the head of the queue in order to have all other threads assist the completion of the batching event.

Our Implementation
There are a few differences between our implementation and the papers original implementation. The first thing was that since the original paper implemented the data structure in C++ it made use of the Union operator which allowed different objects to share the same memory location. Java unfortunately does not have similar functionality do to not having low level memory access. To work around this we had to make some new data structures which we mention in section 4.1.
Another key thing was that certain functionality regarding keeping track of excess dequeues, and certain interface functions were described in the paper, but there was no examples in pseudo code to reference. Because of this, we had to write our own implementation of these methods which resulted in a bit different layout that the original version. This was one of the tougher obstacles we faced in our re-implementation since it require a lot of debugging, and trial and error to get the correct working version of the data structure.

Algorithm Details
The Batching Queue extension to the Lock Free Queue allows for deferred operations to operate in batches of operations rather than having each enqueue and dequeue execute on the shared queue as they are called. The deferring of these operations allows for the user to aggregate pending operations within each thread to be executed at a later time. When a future method is called, either enqueue or dequeue, a Future object is returned to the caller, who may evaluate it later. The execution of operations on the shared queue is delayed until the user explicitly evaluates the future of a deferred operation or a standard queue operation is called. At the point of evaluation, the pending operations of a thread are gathered to a single batched operation to be applied to the shared queue. After which the batch execution is finalized by locally filling in the return values of the pending operations. This locally batched execution and value filling reduces the overhead of having multiple threads compete for alteration of the shared queue. 1. Node Class: The Node class is just a standard node class for a linked list implementation.

Data Structures
2. Future Class: The Future class is going to be containing a result which will be holding the return value of the deferred operation that generated the Future, and a isDone value so we now if the deferred operation has been completed.

NodeWithCount Class:
The NodeWithCount class is a standard node class but with a integer counter attached to perform double CAS operations.

FutureOp Class:
The FutureOp class is used to hold a deferred operation such as an enqueue or a dequeue. It will also hold a copy of a Future to be used for evaluation.

BatchRequest Class:
The BatchRequest class will be prepared by a thread that needs to initiate a batch, and it will hold the details of the batches operations. The fields firstEnq and lastEnq are references to the first and last nodes of the pending items to be put in to the shared queue. While the fields numEnqs, numDeqs, and numExcessDeqs are details about the batch.
6. NodeCountOrAnn Class: The NodeCountOrAnn is used to hold either an Annoouncement or a NodeWith-Count. The class will have a boolean value isAnnouncement if this is set to true, then we are using the announcementfield, and if it false then we are using the nodeWCount field.
7. ThreadData Class: This is the class that will be used to store all the local data for a given thread. The opsQueue is just a standard non thread-safe Queue of FutureOps that is used to store all operations received by a thread. The enqHead and enqTail fields are used to access and keep track of the queue of FutureOps. The last three fields numEnqs, numDeqs, and numExcessDeqsare used to store the prepossessing data so we are able to manage where the head and tail should be pointing after applying a batch.

Algorithm Implementation
The following methods are used internally to apply operations to the shared queue: EnqueueToShared, Dequeue-FromShared and ExecuteBatch. To help a concurrent batch execution and obtain the new head, they call the Hel-pAnnAndGetHead auxiliary method. To carry out a batch, the ExecuteAnn auxiliary method is called. It's caller can be either the batch's initiating thread or a helping thread that encountered the announcement while trying to complete its own operation.
EnqueueToShared: This method appends an item after the tail of the shared queue using two CAS operations. It first updates the shared tail's next node to point to the new node and item, and then updates the shared tail to point to the new node and item. If another thread obstructs operation by enqueueing its own item concurrently, EnqueueToShared will try and help complete the obstructing operation before retrying its own operation. This is possible through the use of two CAS operations to link the node and update the shared tail. This method can be obstructed by either a singular operation or a batch operation.
DequeueFromShare: If the queue is not empty when the dequeue operation takes effect, this method extracts an item from the head of the shared queue and returns it. Otherwise, it returns NULL if taking effect on an empty queue. This method will also help assist ongoing batch operations to complete first through its calling of HelpAnnAndGetHead in its execution.
HelpAnnAndGetHead: This auxiliary method helps announcements to complete their execution by returning the head NodeWithCount if there is no announcement in place or the Announcement object if there is an announcement in progress, allowing the calling thread to assist execution. This method will return the head of the shared queue, which is a NodeCountOrAnnouncement object.
ExecuteBatch: This method is responsible for executing the batch. It receives a BatchRequest object and creates a new announcement for it. Before storing this announcement in the head it checks to see if the head contains an announcement. If the head already contains an announcement, it helps it to complete its execution. Otherwise, this method will replace the head of the shared queue with this new announcement object, encouraging all other threads to help it complete. ExecuteAnnouncement: is called by ExecuteBatch after installing an Announcement object in the shared head or by other threads that otherwise encounter an Announcement object in the shared head. ExecuteAnnouncement will carry out an Announcement's batch. If any of the steps in the execution of the batch have been completed by a competing thread, the method moves on to the next step without taking effect on the shared queue.
The first step of ExecuteAnnouncement is to make sure that the items in the Announcement is linked to the shared queue, and that the old tail to which they were appended has been recorded in the Announcement object. If the items have already been appended to the queue and the old tail has been recorded in the Announcement it is clear that another thread has completed the linking, and the method breaks out of the linkage loop. Otherwise, we try to link the items to the queue though a compare and set operation on the next pointer of the current tail node of the shared queue. We then check whether the compare and set operated succeeded in linking the items. If the items were successfully linked, the old tail reference is stored to signify the successful linkage. Otherwise, we would try and help any other obstructing operations and restart the attempt at the linkage The next step in the method is to update the reference of the shared queue's tail to reflect the newly appended nodes. The last step is to call updateHead so we can update the head of the shared queue to point to the last node that was dequeued by the batch. By doing so, this will also uninstall the announcement that was inserted in to the shared queues head and it will complete its handling.
UpdateHead: This methods main purpose is to update the head to the correct location after a batch operation is complete. The algorithm for this process is as follows: If the number of the batch's successful dequeues is at least the size of of the queue before applying the batch, then the head is determined by over uccess f ulDeqsNum -oldQueueSize nodes, starting at the position of the node pointed to by old tail. Otherwise, we determine it by passing over successfulDeqsNum and by starting at the old dummy node. Finally, the head is moved to the correct position after the batch is applied.
Interface Methods: The following methods are ones that are exposed to the user. They consist of Enqueue, Dequeue, FutureEnqueue, FutureDequeue and Evaluate. The first method Enqueue, is for if the user is just trying to insert a single item in to the shared queue. By calling Enqueue the thread will just push one single item on to the shared queue. Dequeue works similar to the Enqueue method in that it will just attempt to dequeue one item from the shared queue. If the queue is empty then Dequeue will just return null. FutureEnqueue is for the user to create a FutureOp object representing an enqueue operation to be inserted in to the ThreadData opsQueue. FutureEnqueue will also keep track of the number of pending enqueue operations so it knows the amount of enqueues in the batch. FutureDequeue operates in a similar way as FutureEnqueue, but is instead handling dequeue operations and keeping track of the number of pending dequeues. The final method is Evaluate, which receives a Future and makes sure that it is applied when the method returns. If the Future has already been applied from the outset, then the result is immediately returned. Otherwise, all the items in the local ThreadData queue opsQueue will be applied all at once by calling the method ExecuteBatch.

Results
The first performance results we'll talk about is comparing our lock-free implementation of BQ compared to the performance of a standard implementation of a lock-free queue. The goal here was to measure overall run-time on 5000 operations of random enqueue and dequeue operations while also varying the batch size amount and amount of threads. In Figure 1 we see the smallest batch size of 4 perform about expected. With smaller batch sizes, BQ is not fully utilizing the performance increase of batching operations. With batch sizes this small, the standard lock-free queue slightly out performs BQ.
We start to see improvement over the standard lock-free implementation with a batch size of 16. This result was expected because as the batch sizes increase so should the performance of BQ. The last graph is with a batch size of 64, and is where we see the biggest performance increase. Increasing the load from this point forward would just increase the performance of BQ compared to the lock-free queue.

STM Implementation and Results
Another part of our project was creating an STM [30] implementation of our lock-free data structure. Since our implementation was done in Java, we decided to go with the Java STM Deuce [16]. This STM library is older and lacks proper documentation, but makes up for it by being easy to implement. To create transactions in Deuce all you need to do is surround your methods that execute atomic operations with the @Atomic flag, and you can also add a parameter for a certain number of retries to catch any exceptions thrown by the method attempting to execute. This will the re enter the critical section that was previously marked with the @Atomic flag.
Recent advances in transactional data structure design can deliver a more fine-tuned implementation [27,31,18,3,19,24], however this will require the re-engineering of the data structure design and is outside the scope of our current work. Other alternatives to the use of STM are the use of multi-resource locking [32] or a multi-word CAS algorithm [9]. We plan to explore the use of these techniques in our future work. Furthermore, in our future work we plan to explore the guarantee wait-free progress by using the facilities provided by the Tervel library [8].
Per the performance evaluation, we decided to vary the the number of threads and also vary the split. The performance evaluation follows the experimentation methodology for the analysis of non-blocking data structures presented by Izadpanah et. al [13].