并行数据结构可以依赖于自动内存管理器吗?

E. Petrank
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引用次数: 8

摘要

并行数据结构的复杂性通常由两个主要因素来衡量:它们提供的吞吐量和它们保证的进度。对于需要响应性的系统,如实时系统、操作系统、交互系统等,进度保证尤为重要。文献[4,6]中已经提出了进度保证的概念,如锁自由、等待自由和阻碍自由,它们提供了不同级别的保证。对共享对象的并发访问(以及进一步的乐观访问)使得内存管理成为并发算法设计中较为复杂的方面之一。自动内存管理的使用大大简化了这类算法[11,3,2,9]。然而,尽管无锁垃圾收集的存在已经得到了证明,但支持无锁或无等待算法的实用自动内存管理器的存在仍然是开放的。此外,已知的手动回收未使用对象的方案很难使用,并且会给执行带来很大的开销。事实证明,内存管理社区并没有完全意识到对支持并发数据结构设计的进度保证的内存管理器的需求有多么迫切。同样,并发数据结构的设计者并不总是意识到支持进程保证的内存管理是不可用的这一事实。缩小这两个社区之间的差距对两个社区来说都是一个悬而未决的重大问题。在这次演讲中,我们将研究并发算法的内存管理需求。接下来,我们将讨论最先进的研究和实践如何处理缺少重要技术的事实(例如,[7,1])。最后,我们将调查这个谜题中当前可用的部分(例如,[13,12,8])并指定缺少哪些部分。这个悬而未决的问题可以说是当前内存管理社区面临的最大挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can parallel data structures rely on automatic memory managers?
The complexity of parallel data structures is often measured by two major factors: the throughput they provide and the progress they guarantee. Progress guarantees are particularly important for systems that require responsiveness such as real-time systems, operating systems, interactive systems, etc. Notions of progress guarantees such as lock-freedom, wait-freedom, and obstruction-freedom that provide different levels of guarantees have been proposed in the literature [4, 6]. Concurrent access (and furthermore, optimistic access) to shared objects makes the management of memory one of the more complex aspects of concurrent algorithms design. The use of automatic memory management greatly simplifies such algorithms [11, 3, 2, 9]. However, while the existence of lock-free garbage collection has been demonstrated [5], the existence of a practical automatic memory manager that supports lock-free or wait-free algorithms is still open. Furthermore, known schemes for manual reclamation of unused objects are difficult to use and impose a significant overhead on the execution [10]. It turns out that the memory management community is not fully aware of how dire the need is for memory managers that support progress guarantees for the design of concurrent data structures. Likewise, designers of concurrent data structures are not always aware of the fact that memory management with support for progress guarantees is not available. Closing this gap between these two communities is a major open problem for both communities. In this talk we will examine the memory management needs of concurrent algorithms. Next, we will discuss how state-of-the-art research and practice deal with the fact that an important piece of technology is missing (e.g., [7, 1]). Finally, we will survey the currently available pieces in this puzzle (e.g., [13, 12, 8]) and specify which pieces are missing. This open problem is arguably the greatest challenge facing the memory management community today.
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