Concurrent Data Structures Made Easy (Extended Version) (2408.13779v1)

Abstract: Design of an efficient thread-safe concurrent data structure is a balancing act between its implementation complexity and performance. Lock-based concurrent data structures, which are relatively easy to derive from their sequential counterparts and to prove thread-safe, suffer from poor throughput under even light multi-threaded workload. At the same time, lock-free concurrent structures allow for high throughput, but are notoriously difficult to get right and require careful reasoning to formally establish their correctness. We explore a solution to this conundrum based on batch parallelism, an approach for designing concurrent data structures via a simple insight: efficiently processing a batch of a priori known operations in parallel is easier than optimising performance for a stream of arbitrary asynchronous requests. Alas, batch-parallel structures have not seen wide practical adoption due to (i) the inconvenience of having to structure multi-threaded programs to explicitly group operations and (ii) the lack of a systematic methodology to implement batch-parallel structures as simply as lock-based ones. We present OBatcher-an OCaml library that streamlines the design, implementation, and usage of batch-parallel structures. It solves the first challenge (how to use) by suggesting a new lightweight implicit batching design that is built on top of generic asynchronous programming mechanisms. The second challenge (how to implement) is addressed by identifying a family of strategies for converting common sequential structures into efficient batch-parallel ones. We showcase OBatcher with a diverse set of benchmarks. Our evaluation of all the implementations on large asynchronous workloads shows that (a) they consistently outperform the corresponding coarse-grained lock-based implementations and that (b) their throughput scales reasonably with the number of processors.

• The paper introduces OBatcher, a novel framework that leverages implicit batching and flat combining to simplify the development of concurrent data structures.
• It demonstrates language-agnostic implementations in OCaml and Rust, showcasing its versatility for diverse system environments.
• The evaluation shows that optimized batch sizes significantly enhance throughput under high concurrency while ensuring correctness through the expose-repair strategy.

Concurrent Data Structures Made Easy

Overview of the Paper

The paper entitled "Concurrent Data Structures Made Easy" presents a pragmatic and language-agnostic framework for implementing concurrent data structures, focusing specifically on implicit batching and flat combining techniques. The authors introduce and evaluate OBatcher, a novel framework that simplifies the development of concurrent data structures by abstracting complexities inherent to concurrency control. They demonstrate the framework's applicability and general patterns through implementations in OCaml and Rust, making a case for its versatility.

Key Contributions

1. Implicit Batching and Flat Combining: The paper elaborates on the differences between implicit batching and flat combining, detailing how the former is leveraged within the OBatcher framework. The clear exposition of this relationship aids in distinguishing OBatcher's novel contributions from existing techniques.
2. Language Agnostic Implementation: By removing Section 3.1 and adding a new section on Rust encoding, the authors reinforce that OBatcher's patterns are not tied to a specific programming language. This enhances the paper's appeal to a broader audience and its potential for wide adoption in various system environments.
3. Expose-Repair Correctness: A semi-formal argument for the correctness of the expose-repair pattern is provided. Illustrated with a Van Emde Boas Tree case paper, this argument substantiates OBatcher's reliability and correctness in implementing complex concurrent data structures.
4. Evaluation and Optimizations: In the evaluation section, the authors discuss sequential optimizations applicable to their batched skiplist. They also clarify the impact of the minimum batch size on performance in highly concurrent workloads, emphasizing how OBatcher optimizes these parameters for enhanced throughput.

Numerical Results and Claims

The modified evaluation section discusses how implementing the batched skiplist in OBatcher can yield performance benefits, particularly in high concurrency scenarios. While the specific numerical results are not detailed in the revision feedback, the emphasis on enhanced throughput due to optimized batch sizes underscores OBatcher's efficacy. The comparison against flat combining and other techniques further contextualizes these claims within existing research, providing a robust foundation for their assertions.

Practical and Theoretical Implications

Practically, the framework offers a simplified and reusable pattern for developers, potentially reducing the time and effort needed to implement high-performance concurrent data structures. By supporting multiple programming languages, OBatcher stands to facilitate integration into diverse systems, promoting widespread adoption.

Theoretically, the formal discussion on correctness and the abstraction of concurrency complexities provide a foundation for future research in concurrent data structures. The ability to easily switch between programming languages without loss of performance or correctness makes OBatcher a valuable tool for exploring new concurrency paradigms.

Future Developments

Future developments could include extending the framework to support additional languages beyond OCaml and Rust, thereby increasing its applicability. Furthermore, empirical studies comparing OBatcher with other concurrency models across various hardware configurations could provide deeper insights into its performance characteristics.

In the field of AI, the principles derived from OBatcher could inform the design of concurrent algorithms in machine learning frameworks, where parallelism and concurrency play crucial roles.

Conclusion

"Concurrent Data Structures Made Easy" offers a significant contribution to the field of concurrent programming by introducing a versatile and effective framework. With its emphasis on practical applicability and theoretical robustness, the paper paves the way for future research and development in concurrent data structures, promising improved efficiency and ease of implementation across diverse computing environments.