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Adapting Persistent Data Structures for Concurrency and Speculation
arXiv - CS - Data Structures and Algorithms Pub Date : 2020-03-16 , DOI: arxiv-2003.07395 Thomas Dickerson
arXiv - CS - Data Structures and Algorithms Pub Date : 2020-03-16 , DOI: arxiv-2003.07395 Thomas Dickerson
This work unifies insights from the systems and functional programming
communities, in order to enable compositional reasoning about software which is
nonetheless efficiently realizable in hardware. It exploits a correspondence
between design goals for efficient concurrent data structures and efficient
immutable persistent data structures, to produce novel implementations of
mutable concurrent trees with low contention and an efficient snapshot
operation to support speculative execution models. It also exploits
commutativity to characterize a design space for integrating traditional
high-performance concurrent data structures into Software Transactional Memory
(STM) runtimes, and extends this technique to yield a novel algorithm for
concurrent execution of so-called ``smart contracts'' (specialized programs
which manipulate the state of blockchain ledgers).
中文翻译:
为并发和推测调整持久数据结构
这项工作统一了来自系统和函数式编程社区的见解,以实现关于软件的组合推理,而这种推理仍然可以在硬件中有效地实现。它利用了高效并发数据结构的设计目标与高效的不可变持久数据结构的设计目标之间的对应关系,以产生具有低争用和高效快照操作的可变并发树的新颖实现,以支持推测执行模型。它还利用交换性来表征将传统高性能并发数据结构集成到软件事务内存 (STM) 运行时的设计空间,并扩展该技术以产生一种用于并发执行所谓“智能合约”的新算法
更新日期:2020-03-18
中文翻译:
为并发和推测调整持久数据结构
这项工作统一了来自系统和函数式编程社区的见解,以实现关于软件的组合推理,而这种推理仍然可以在硬件中有效地实现。它利用了高效并发数据结构的设计目标与高效的不可变持久数据结构的设计目标之间的对应关系,以产生具有低争用和高效快照操作的可变并发树的新颖实现,以支持推测执行模型。它还利用交换性来表征将传统高性能并发数据结构集成到软件事务内存 (STM) 运行时的设计空间,并扩展该技术以产生一种用于并发执行所谓“智能合约”的新算法