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

Lock-Free and Practical Deques using Single-Word Compare-And-Swap

We present an efficient and practical lock-free implementation of a concurrent deque that is disjoint-parallel accessible and uses atomic primitives which are available in modern computer systems. Previously known lock-free algorithms of deques are either based on non-available atomic synchronization primitives, only implement a subset of the functionality, or are not designed for disjoint accesses. Our algorithm is based on a doubly linked list, and only requires single-word compare-and-swap atomic primitives, even for dynamic memory sizes. We have performed an empirical study using full implementations of the most efficient algorithms of lock-free deques known. For systems with low concurrency, the algorithm by Michael shows the best performance. However, as our algorithm is designed for disjoint accesses, it performs significantly better on systems with high concurrency and non-uniform memory architecture

CheckFence: Checking Consistency of Concurrent Data Types on Relaxed Memory Models

Concurrency libraries can facilitate the development of multithreaded programs by providing concurrent implementations of familiar data types such as queues or sets. There exist many optimized algorithms that can achieve superior performance on multiprocessors by allowing concurrent data accesses without using locks. Unfortunately, such algorithms can harbor subtle concurrency bugs. Moreover, they require memory ordering fences to function correctly on relaxed memory models. To address these difficulties, we propose a verification approach that can exhaustively check all concurrent executions of a given test program on a relaxed memory model and can verify that they are observationally equivalent to a sequential execution. Our Check- Fence prototype automatically translates the C implementation code and the test program into a SAT formula, hands the latter to a standard SAT solver, and constructs counterexample traces if there exist incorrect executions. Applying CheckFence to five previously published algorithms, we were able to (1) find several bugs (some not previously known), and (2) determine how to place memory ordering fences for relaxed memory models

Polynomial-Time Verification and Testing of Implementations of the Snapshot Data Structure

We analyze correctness of implementations of the snapshot data structure in terms of linearizability. We show that such implementations can be verified in polynomial time. Additionally, we identify a set of representative executions for testing and show that the correctness of each of these executions can be validated in linear time. These results present a significant speedup considering that verifying linearizability of implementations of concurrent data structures, in general, is EXPSPACE-complete in the number of program-states, and testing linearizability is NP-complete in the length of the tested execution. The crux of our approach is identifying a class of executions, which we call simple, such that a snapshot implementation is linearizable if and only if all of its simple executions are linearizable. We then divide all possible non-linearizable simple executions into three categories and construct a small automaton that recognizes each category. We describe two implementations (one for verification and one for testing) of an automata-based approach that we develop based on this result and an evaluation that demonstrates significant improvements over existing tools. For verification, we show that restricting a state-of-the-art tool to analyzing only simple executions saves resources and allows the analysis of more complex cases. Specifically, restricting attention to simple executions finds bugs in 27 instances, whereas, without this restriction, we were only able to find 14 of the 30 bugs in the instances we examined. We also show that our technique accelerates testing performance significantly. Specifically, our implementation solves the complete set of 900 problems we generated, whereas the state-of-the-art linearizability testing tool solves only 554 problems

Efficient Multi-Word Compare and Swap

Atomic lock-free multi-word compare-and-swap (MCAS) is a powerful tool for designing concurrent algorithms. Yet, its widespread usage has been limited because lock-free implementations of MCAS make heavy use of expensive compare-and-swap (CAS) instructions. Existing MCAS implementations indeed use at least 2k+1 CASes per k-CAS. This leads to the natural desire to minimize the number of CASes required to implement MCAS. We first prove in this paper that it is impossible to "pack" the information required to perform a k-word CAS (k-CAS) in less than k locations to be CASed. Then we present the first algorithm that requires k+1 CASes per call to k-CAS in the common uncontended case. We implement our algorithm and show that it outperforms a state-of-the-art baseline in a variety of benchmarks in most considered workloads. We also present a durably linearizable (persistent memory friendly) version of our MCAS algorithm using only 2 persistence fences per call, while still only requiring k+1 CASes per k-CAS

Parallel bug-finding in concurrent programs via reduced interleaving instances

Concurrency poses a major challenge for program verification, but it can also offer an opportunity to scale when subproblems can be analysed in parallel. We exploit this opportunity here and use a parametrizable code-to-code translation to generate a set of simpler program instances, each capturing a reduced set of the original program’s interleavings. These instances can then be checked independently in parallel. Our approach does not depend on the tool that is chosen for the final analysis, is compatible with weak memory models, and amplifies the effectiveness of existing tools, making them find bugs faster and with fewer resources. We use Lazy-CSeq as an off-the-shelf final verifier to demonstrate that our approach is able, already with a small number of cores, to find bugs in the hardest known concurrency benchmarks in a matter of minutes, whereas other dynamic and static tools fail to do so in hours

Non-blocking Priority Queue based on Skiplists with Relaxed Semantics

Priority queues are data structures that store information in an orderly fashion. They are of tremendous importance because they are an integral part of many applications, like Dijkstra’s shortest path algorithm, MST algorithms, priority schedulers, and so on. Since priority queues by nature have high contention on the delete_min operation, the design of an efficient priority queue should involve an intelligent choice of the data structure as well as relaxation bounds on the data structure. Lock-free data structures provide higher scalability as well as progress guarantee than a lock-based data structure. That is another factor to be considered in the priority queue design. We present a relaxed non-blocking priority queue based on skiplists. We address all the design issues mentioned above in our priority queue. Use of skiplists allows multiple threads to concurrently access different parts of the skiplist quickly, whereas relaxing the priority queue delete_min operation distributes contention over the skiplist instead of just at the front. Furthermore, a non-blocking implementation guarantees that the system will make progress even when some process fails. Our priority queue is internally composed of several priority queues, one for each thread and one shared priority queue common to all threads. Each thread selects the best value from its local priority queue and the shared priority queue and returns the value. In case a thread is unable to delete an item, it tries to spy items from other threads\u27 local priority queues. We experimentally and theoretically show the correctness of our data structure. We also compare the performance of our data structure with other variations like priority queues based on coarse-grained skiplists for both relaxed and non-relaxed semantics

Achieving Efficient Work-Stealing for Data-Parallel Collections

In modern programming high-level data-structures are an important foundation for most applications. With the rise of the multi-core era, there is a growing trend of supporting data-parallel collection operations in general purpose programming languages and platforms. To facilitate object-oriented reuse these operations are highly parametric, incurring abstraction performance penalties. Furthermore, data-parallel operations must scale when used in problems with irregular workloads. Work-stealing is a proven load-balancing technique when it comes to irregular workloads, but general purpose work-stealing also suffers from abstraction penalties. In this paper we present a generic design of a data-parallel collections framework based on work-stealing for shared-memory architectures. We show how abstraction penalties can be overcome through callsite specialization of data-parallel operations instances. Moreover, we show how to make work-stealing fine-grained and efficient when specialized for particular data-structures. We experimentally validate the performance of different data-structures and data-parallel operations, achieving up to 60X better performance with abstraction penalties eliminated and 3X higher speedups by specializing work-stealing compared to existing approaches