Lock-free Concurrent Data Structures

Concurrent data structures are the data sharing side of parallel programming. Data structures give the means to the program to store data, but also provide operations to the program to access and manipulate these data. These operations are implemented through algorithms that have to be efficient. In the sequential setting, data structures are crucially important for the performance of the respective computation. In the parallel programming setting, their importance becomes more crucial because of the increased use of data and resource sharing for utilizing parallelism. The first and main goal of this chapter is to provide a sufficient background and intuition to help the interested reader to navigate in the complex research area of lock-free data structures. The second goal is to offer the programmer familiarity to the subject that will allow her to use truly concurrent methods.Comment: To appear in "Programming Multi-core and Many-core Computing Systems", eds. S. Pllana and F. Xhafa, Wiley Series on Parallel and Distributed Computin

Concurrent Data Structures Linked in Time

Arguments about correctness of a concurrent data structure are typically carried out by using the notion of linearizability and specifying the linearization points of the data structure's procedures. Such arguments are often cumbersome as the linearization points' position in time can be dynamic (depend on the interference, run-time values and events from the past, or even future), non-local (appear in procedures other than the one considered), and whose position in the execution trace may only be determined after the considered procedure has already terminated. In this paper we propose a new method, based on a separation-style logic, for reasoning about concurrent objects with such linearization points. We embrace the dynamic nature of linearization points, and encode it as part of the data structure's auxiliary state, so that it can be dynamically modified in place by auxiliary code, as needed when some appropriate run-time event occurs. We name the idea linking-in-time, because it reduces temporal reasoning to spatial reasoning. For example, modifying a temporal position of a linearization point can be modeled similarly to a pointer update in separation logic. Furthermore, the auxiliary state provides a convenient way to concisely express the properties essential for reasoning about clients of such concurrent objects. We illustrate the method by verifying (mechanically in Coq) an intricate optimal snapshot algorithm due to Jayanti, as well as some clients

Quantitative relaxation of concurrent data structures

There is a trade-off between performance and correctness in implementing concurrent data structures. Better performance may be achieved at the expense of relaxing correctness, by redefining the semantics of data structures. We address such a redefinition of data structure semantics and present a systematic and formal framework for obtaining new data structures by quantitatively relaxing existing ones. We view a data structure as a sequential specification S containing all "legal" sequences over an alphabet of method calls. Relaxing the data structure corresponds to defining a distance from any sequence over the alphabet to the sequential specification: the k-relaxed sequential specification contains all sequences over the alphabet within distance k from the original specification. In contrast to other existing work, our relaxations are semantic (distance in terms of data structure states). As an instantiation of our framework, we present two simple yet generic relaxation schemes, called out-of-order and stuttering relaxation, along with several ways of computing distances. We show that the out-of-order relaxation, when further instantiated to stacks, queues, and priority queues, amounts to tolerating bounded out-of-order behavior, which cannot be captured by a purely syntactic relaxation (distance in terms of sequence manipulation, e.g. edit distance). We give concurrent implementations of relaxed data structures and demonstrate that bounded relaxations provide the means for trading correctness for performance in a controlled way. The relaxations are monotonic which further highlights the trade-off: increasing k increases the number of permitted sequences, which as we demonstrate can lead to better performance. Finally, since a relaxed stack or queue also implements a pool, we actually have new concurrent pool implementations that outperform the state-of-the-art ones

A VLSI Architecture for Concurrent Data Structures

Concurrent data structures simplify the development of concurrent programs by encapsulating commonly used mechanisms for synchronization and communication into data structures. This thesis develops a notation for describing concurrent data structures, presents examples of concurrent data structures, and describes an architecture to support concurrent data structures. Concurrent Smalltalk (CST), a derivative of Smalltalk-80 with extensions for concurrency, is developed to describe concurrent data structures. CST allows the programmer to specify objects that are distributed over the nodes of a concurrent computer. These distributed objects have many constituent objects and thus can process many messages simultaneously. They are the foundation upon which concurrent data structures are built. The balanced cube is a concurrent data structure for ordered sets. The set is distributed by a balanced recursive partition that maps to the subcubes of a binary n-cube using a Gray code. A search algorithm, VW search, based on the distance properties of the Gray code, searches a balanced cube in O(log N) time. Because it does not have the root bottleneck that limits all tree-based data structures to O(1) concurrency, the balanced cube achieves O(~og ) concurrency. Considering graphs as concurrent data structures, graph algorithms are presented for the shortest path problem, the mix-flow problem, and graph partitioning. These algorithms introduce new synchronization techniques to achieve better performance than existing algorithms. A message-passing, concurrent architecture is developed that exploits the characteristics of VLSI technology to support concurrent data structures. Interconnection topologies are compared on the basis of dimension. It is shown that minimum latency is achieved with a very low dimensional network. A deadlock-free routing strategy is developed for this class of networks, and a prototype VLSI chip implementing this strategy is described. A message-driven processor complements the network by responding to messages with a very low latency. The processor directly executes messages, eliminating a level of interpretation. To take advantage of the performance offered by specialization while at the same time retaining flexibility, processing elements can be specialized to operate on a single class of objects. These object experts accelerate the performance of all applications using this class

A History of BlockingQueues

This paper describes a way to formally specify the behaviour of concurrent data structures. When specifying concurrent data structures, the main challenge is to make specifications stable, i.e., to ensure that they cannot be invalidated by other threads. To this end, we propose to use history-based specifications: instead of describing method behaviour in terms of the object's state, we specify it in terms of the object's state history. A history is defined as a list of state updates, which at all points can be related to the actual object's state. We illustrate the approach on the BlockingQueue hierarchy from the java.util.concurrent library. We show how the behaviour of the interface BlockingQueue is specified, leaving a few decisions open to descendant classes. The classes implementing the interface correctly inherit the specifications. As a specification language, we use a combination of JML and permission-based separation logic, including abstract predicates. This results in an abstract, modular and natural way to specify the behaviour of concurrent queues. The specifications can be used to derive high-level properties about queues, for example to show that the order of elements is preserved. Moreover, the approach can be easily adapted to other concurrent data structures.Comment: In Proceedings FLACOS 2012, arXiv:1209.169