Cache-line transactions: Building blocks for persistent kernel data structures enabled by AspecTC++

Autor(en): Köppen, M.
Traue, J.
Borchert, C.
Nolte, J.
Spinczyk, O. 
Stichwörter: Aspect-oriented programming; Building blockes; Cache memory; Complex data structures; Data consistency; Data manipulations; Data structures; Non-volatile memory; Nonvolatile storage; Object oriented programming; Persistent data structures; System failures; Systems engineering, Address space; Transaction mechanism, Aspect oriented programming
Erscheinungsdatum: 2019
Herausgeber: Association for Computing Machinery, Inc
Journal: PLOS 2019 - Proceedings of the 10th Workshop on Programming Languages and Operating Systems, Part of SOSP 2019
Startseite: 38
Seitenende: 44
Zusammenfassung: 
With the availability of systems that contain large amounts of byte-addressable non-volatile memory (NVRAM), there is a growing need for data structures that can be mapped into a process's address space and be used without data (de-)serialization. While NVRAM is able to retain memory contents during system failure and power loss, data consistency has to be preserved by using transactional operations for data manipulation. This paper describes a lightweight and efficient transaction mechanism for small data structures in memory-mapped NVRAM. The size per data structure is limited to half a cache-line, so that the approach cannot serve as a general purpose mechanism for arbitrary applications, but could be used within an operating system as a low-level building block for more complex data structures. By using aspect-oriented programming with AspectC++, the mechanism can be used in an almost transparent manner, which helps to avoid many possible sources for bugs. © 2019 Copyright held by the owner/author(s).
Beschreibung: 
Conference of 10th Workshop on Programming Languages and Operating Systems, PLOS 2019, held in conjunction with the 27th ACM Symposium on Operating Systems Principles, SOSP 2019 ; Conference Date: 27 October 2019; Conference Code:154261
ISBN: 9781450370172
DOI: 10.1145/3365137.3365396
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075744421&doi=10.1145%2f3365137.3365396&partnerID=40&md5=bf1e7edcc19496176acad84ebf64a158

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