{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/concurrent-data-structures-made-easy-extended","title":"Concurrent Data Structures Made Easy (Extended Version)","arxiv_id":"2408.13779","date":"2024-08-25","proceeding":null,"authors":["Callista Le","Kiran Gopinathan","Koon Wen Lee","Seth Gilbert","Ilya Sergey"],"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.","url_abs":"https://arxiv.org/abs/2408.13779v1","url_pdf":"https://arxiv.org/pdf/2408.13779v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"concurrent-data-structures-made-easy-extended","repo_url":"https://github.com/verse-lab/obatcher_ds","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}