A Single-Enqueuer Wait-Free Queue Implementation

Abstract. We study wait-free linearizable Queue implementations in asynchronous shared-memory systems from other consensus number 2 objects, such as Fetch&Add and Swap. The best previously known im-plementation allows at most two processes to perform Dequeue opera-tions. We provide a new implementation, when only one process performs Enqueue operations and any number of processes perform Dequeue op-erations. A nice feature of this implementation is the fact that both Enqueue and Dequeue operations take constant time.

A Scalable, Portable, and Memory-Efficient Lock-Free FIFO Queue

We present a new lock-free multiple-producer and multiple-consumer (MPMC) FIFO queue design which is scalable and, unlike existing high-performant queues, very memory efficient. Moreover, the design is ABA safe and does not require any external memory allocators or safe memory reclamation techniques, typically needed by other scalable designs. In fact, this queue itself can be leveraged for object allocation and reclamation, as in data pools. We use FAA (fetch-and-add), a specialized and more scalable than CAS (compare-and-set) instruction, on the most contended hot spots of the algorithm. However, unlike prior attempts with FAA, our queue is both lock-free and linearizable. We propose a general approach, SCQ, for bounded queues. This approach can easily be extended to support unbounded FIFO queues which can store an arbitrary number of elements. SCQ is portable across virtually all existing architectures and flexible enough for a wide variety of uses. We measure the performance of our algorithm on the x86-64 and PowerPC architectures. Our evaluation validates that our queue has exceptional memory efficiency compared to other algorithms and its performance is often comparable to, or exceeding that of state-of-the-art scalable algorithms

Efficient Wait-Free Queue Algorithms with Multiple Enqueuers and Multiple Dequeuers

Despite the widespread usage of FIFO queues in distributed applications, designing efficient wait-free implementations of queues remains a challenge. The majority of wait-free queue implementations restrict either the number of dequeuers or the number of enqueuers that can operate on the queue, even when they use strong synchronization primitives, like the Compare&Swap. If we do not limit the number of processes that can perform enqueue and dequeue operations, the best-known upper bound on the worst case step complexity for a wait-free queue is given by [Khanchandani and Wattenhofer, 2018]. In particular, they present an implementation of a multiple dequeuer multiple enqueuer wait-free queue whose worst case step complexity is in O(?n), where n is the number of processes. In this work, we investigate whether it is possible to improve this bound. In particular, we present a wait-free FIFO queue implementation that supports n enqueuers and k dequeuers where the worst case step complexity of an Enqueue operation is in O(log n) and of a Dequeue operation is in O(k log n). Then, we show that if the semantics of the queue can be relaxed, by allowing concurrent Dequeue operations to retrieve the same element, then we can achieve O(log n) worst-case step complexity for both the Enqueue and Dequeue operations

Addressing Queuing Bottlenecks at High Speeds

Modern routers and switch fabrics can have hundreds of input and output ports running at up to 10 Gb/s; 40 Gb/s systems are starting to appear. At these rates, the performance of the buffering and queuing subsystem becomes a significant bottleneck. In high performance routers with more than a few queues, packet buffering is typically implemented using DRAM for data storage and a combination of off-chip and on-chip SRAM for storing the linked-list nodes and packet length, and the queue headers, respectively. This paper focuses on the performance bottlenecks associated with the use of off-chip SRAM. We show how the combination of implicit buffer pointers and multi-buffer list nodes can dramatically reduce the impact of buffering and queuing subsystem on queuing performance. We also show how combining it with coarse-grained scheduling can improve the performance of fair queuing algorithms, while also reducing the amount of off-chip memory and bandwidth needed. These techniques can reduce the amount of SRAM needed to hold the list nodes by a factor of 10 at the cost of about 10% wastage of the DRAM space, assuming an aggregation degree of 16

Brief Announcement: Jiffy: A Fast, Memory Efficient, Wait-Free Multi-Producers Single-Consumer Queue

In applications such as sharded data processing systems, data flow programming and load sharing applications, multiple concurrent data producers are feeding requests into the same data consumer. This can be naturally realized through concurrent queues, where each consumer pulls its tasks from its dedicated queue. For scalability, wait-free queues are often preferred over lock based structures. The vast majority of wait-free queue implementations, and even lock-free ones, support the multi-producer multi-consumer model. Yet, this comes at a premium, since implementing wait-free multi-producer multi-consumer queues requires utilizing complex helper data structures. The latter increases the memory consumption of such queues and limits their performance and scalability. Additionally, many such designs employ (hardware) cache unfriendly memory access patterns. In this work we study the implementation of wait-free multi-producer single-consumer queues. Specifically, we propose Jiffy, an efficient memory frugal novel wait-free multi-producer single-consumer queue and formally prove its correctness. We then compare the performance and memory requirements of Jiffy with other state of the art lock-free and wait-free queues. We show that indeed Jiffy can maintain good performance with up to 128 threads, delivers better throughput than other constructions we compared against, and consumes less memory

Nontrivial and Universal Helping for Wait-Free Queues and Stacks

This paper studies two approaches to formalize helping in wait-free implementations of shared objects. The first approach is based on operation valency, and it allows us to make the important distinction between trivial and nontrivial helping. We show that a wait-free implementation of a queue from common2 objects (e.g., Test&Set) requires nontrivial helping. In contrast, there is a wait-free implementation of a stack from Common2 objects with only trivial helping. This separation might shed light on the difficulty of implementing a queue from Common2 objects. The other approach formalizes the helping mechanism employed by Herlihy\u27s universal wait-free construction and is based on having an operation by one process restrict the possible linearizations of operations by other processes. We show that objects possessing such universal helping can be used to solve consensus

Relaxed Queues and Stacks from Read/Write Operations

Considering asynchronous shared memory systems in which any number of processes may crash, this work identifies and formally defines relaxations of queues and stacks that can be non-blocking or wait-free while being implemented using only read/write operations. Set-linearizability and Interval-linearizability are used to specify the relaxations formally, and precisely identify the subset of executions which preserve the original sequential behavior. The relaxations allow for an item to be returned more than once by different operations, but only in case of concurrency; we call such a property multiplicity. The stack implementation is wait-free, while the queue implementation is non-blocking. Interval-linearizability is used to describe a queue with multiplicity, with the additional relaxation that a dequeue operation can return weak-empty, which means that the queue might be empty. We present a read/write wait-free interval-linearizable algorithm of a concurrent queue. As far as we know, this work is the first that provides formalizations of the notions of multiplicity and weak-emptiness, which can be implemented on top of read/write registers only

A Wait-free Queue with Poly-logarithmic Worst-case Step Complexity

In this work, we introduce a novel linearizable wait-free queue implementation. Linearizability and lock-freedom are standard requirements for designing shared data structures. To the best of our knowledge, all of the existing linearizable lock-free queues in the literature have a common problem in their worst case, called the CAS Retry Problem. We show that our algorithm avoids this problem with the helping mechanism which we use and has a worst-case running time better than prior lock-free queues. The amortized number of steps for an Enqueue or Dequeue in our algorithm is O(log^2 p + log q), where p is the number of processes and q is the size of the queue when the operation is linearized