Sequentially consistent versus linearizable counting networks

We compare the impact of timing conditions on implementing sequentially consistent and linearizable counters using (uniform) counting networks in distributed systems. For counting problems in application domains which do not require linearizability but will run correctly if only sequential consistency is provided, the results of our investigation, and their potential payoffs, are threefold: • First, we show that sequential consistency and linearizability cannot be distinguished by the timing conditions previously considered in the context of counting networks; thus, in contexts where these constraints apply, it is possible to rely on the stronger semantics of linearizability, which simplifies proofs and enhances compositionality. • Second, we identify local timing conditions that support sequential consistency but not linearizability; thus, we suggest weaker, easily implementable timing conditions that are likely to be sufficient in many applications. • Third, we show that any kind of synchronization that is too weak to support even sequential consistency may violate it significantly for some counting networks; hence

The impact of timing on linearizability in counting networks

{\em Counting networks} form a new class of distributed, low-contention data structures, made up of {\em balancers} and {\em wires,} which are suitable for solving a variety of multiprocessor synchronization problems that can be expressed as counting problems. A {\em linearizable} counting network guarantees that the order of the values it returns respects the real-time order they were requested. Linearizability significantly raises the capabilities of the network, but at a possible price in network size or synchronization support. In this work, we further pursue the systematic study of the impact of {\em timing} assumptions on linearizability for counting networks, along the line of research recently initiated by Lynch~{\em et~al.} in [18]. We consider two basic {\em timing} models, the {instantaneous balancer} model, in which the transition of a token from an input to an output port of a balancer is modeled as an instantaneous event, and the {\em periodic balancer} model, where balancers send out tokens at a fixed rate. In both models, we assume lower and upper bounds on the delays incurred by wires connecting the balancers. We present necessary and sufficient conditions for linearizability in these models, in the form of precise inequalities that involve not only parameters of the timing models, but also certain structural parameters of the counting network, which may be of more general interest. Our results extend and strengthen previous impossibility and possibility results on linearizability in counting networks

Hoare-style Specifications as Correctness Conditions for Non-linearizable Concurrent Objects

Designing scalable concurrent objects, which can be efficiently used on multicore processors, often requires one to abandon standard specification techniques, such as linearizability, in favor of more relaxed consistency requirements. However, the variety of alternative correctness conditions makes it difficult to choose which one to employ in a particular case, and to compose them when using objects whose behaviors are specified via different criteria. The lack of syntactic verification methods for most of these criteria poses challenges in their systematic adoption and application. In this paper, we argue for using Hoare-style program logics as an alternative and uniform approach for specification and compositional formal verification of safety properties for concurrent objects and their client programs. Through a series of case studies, we demonstrate how an existing program logic for concurrency can be employed off-the-shelf to capture important state and history invariants, allowing one to explicitly quantify over interference of environment threads and provide intuitive and expressive Hoare-style specifications for several non-linearizable concurrent objects that were previously specified only via dedicated correctness criteria. We illustrate the adequacy of our specifications by verifying a number of concurrent client scenarios, that make use of the previously specified concurrent objects, capturing the essence of such correctness conditions as concurrency-aware linearizability, quiescent, and quantitative quiescent consistency. All examples described in this paper are verified mechanically in Coq.Comment: 18 page

Quantifiability: Concurrent Correctness from First Principles

Architectural imperatives due to the slowing of Moore\u27s Law, the broad acceptance of relaxed semantics and the O(n!) worst case verification complexity of sequential histories motivate a new approach to concurrent correctness. Desiderata for a new correctness condition are that it be independent of sequential histories, compositional over objects, flexible as to timing, modular as to semantics and free of inherent locking or waiting. This dissertation proposes Quantifiability, a novel correctness condition based on intuitive first principles. Quantifiablity is formally defined with its system model. Useful properties of quantifiability such as compositionality, measurablility and observational refinement are demonstrated. Quantifiability models a system in vector space to launch a new mathematical analysis of concurrency. The vector space model is suitable for a wide range of concurrent systems and their associated data structures. Proof of correctness is facilitated with linear algebra, better supported and of more efficient time complexity than traditional combinatorial methods. Experimental results are presented showing that quantifiable data structures are highly scalable due to their use of relaxed semantics, an implementation trade-off that is explicitly permitted by quantifiability. The speedups attainable are theoretically analyzed. Because previous work lacked a metric for evaluating such trade-offs, a new measure is proposed here that applies communication theory to the disordered results of concurrent data structures. This entropy measure opens the way to analyze degrees of concurrent correctness across implementations to engineer system scalability and evaluate data structure quality under different workloads. With all its innovation, quantifiability is presented the context of previous work and existing correctness conditions

Randomized versus Deterministic Implementations of Concurrent Data Structures

One of the key trends in computing over the past two decades has been increased distribution, both at the processor level, where multi-core architectures are now the norm, and at the system level, where many key services are currently distributed overmultiple machines. Thus, understanding the power and limitations of computing in a concurrent, distributed setting is one of the major challenges in Computer Science. In this thesis, we analyze the complexity of implementing concurrent data structures in asynchronous shared memory systems. We focus on the complexity of a classic distributed coordination task called renaming, in which a set of processes need to pick distinct names from a small set of identifiers. We present the first tight bounds for the time complexity of this problem, both for deterministic and randomized implementations, solving a long-standing open problem in the field. For deterministic algorithms, we prove a tight linear lower bound; for randomized solutions, we provide logarithmic upper and lower bounds on time complexity. Together, these results show an exponential separation between deterministic and randomized renaming solutions. Importantly, the lower bounds extend to implementations of practical shared-memory data structures, such as queues, stacks, and counters. From a technical perspective, this thesis highlights new connections between the distributed renaming problem and other fundamental objects, such as sorting networks, mutual exclusion, and counters. In particular, we show that sorting networks can be used to obtain optimal randomized solutions to renaming, and that, in turn, the existence of these solutions implies a linear lower bound on the complexity of the problem. In sum, the results in this thesis suggest that deterministic implementations of shared-memory data structures do not scale well in terms of worst-case time complexity. On the positive side, we emphasize randomization as a natural alternative, which can circumvent the deterministic lower bounds with high probability. Thus, a promising direction for future work is to extend our randomized renaming techniques to obtain efficient implementations of concurrent data structures