Tracking in Order to Recover - Detectable Recovery of Lock-Free Data Structures

Attiya, Hagit; Ben-Baruch, Ohad; Fatourou, Panagiota; Hendler, Danny; Kosmas, Eleftherios

doi:10.1145/3350755.3400257

Cited by 11 publications

(18 citation statements)

References 10 publications

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“…Our experiments show that both, PBcomb and PWFcomb, outperform by far, many previous persistent Transactional Memory (TM) Systems [12,13,33,34] and several generic mechanisms for designing persistent data structures [1,2,7] proposed in the literature. Specifically, PBcomb is 4x faster and PWFcomb is 2.8x faster than the competitors.…”

Section: Introductionmentioning

confidence: 77%

“…All the above algorithms satisfy consistency conditions weaker than detectable recoverability ensured by PBcomb and PWFcomb. Generic approaches for building persistent data structures that ensure detectable recoverability appear in [2,7]. Capsules [7] can be applied to concurrent algorithms that use only read and CAS operations.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, PBcomb is 4x faster and PWFcomb is 2.8x faster than the competitors. Our protocols satisfy detectability [20], whereas most competitors (all but those in [7,2]) guarantee only weaker consistency, such as durable linearizability [24].…”

Section: Introductionmentioning

confidence: 99%

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Persistent Software Combining

Fatourou¹,

Kallimanis²,

Kosmas³

2021

Preprint

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We study the performance power of software combining in designing persistent algorithms and data structures. We present Bcomb, a new blocking highly-efficient combining protocol, and built upon it to get PBcomb, a persistent version of it that performs a small number of persistence instructions and exhibits low synchronization cost. We built fundamental recoverable data structures, such as stacks and queues based on PBcomb, as well as on PWFcomb, a wait-free universal construction we present. Our experiments show that PBcomb and PWFcomb outperform by far state-of-the-art recoverable universal constructions and transactional memory systems, many of which ensure weaker consistency properties than our algorithms. We built recoverable queues and stacks, based on PBcomb and PWFcomb, and present experiments to show that they have much better performance than previous recoverable implementations of stacks and queues. We build the first recoverable implementation of a concurrent heap and present experiments to show that it has good performance when the size of the heap is not very large.

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Section: Introductionmentioning

confidence: 77%

Section: Related Workmentioning

confidence: 99%

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Persistent Software Combining

Fatourou¹,

Kallimanis²,

Kosmas³

2021

Preprint

Self Cite

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“…Researchers have also introduced other correctness criteria for persistence and explored how to support them efficiently [6,17,7,4,3]. These works consider not only the state of shared memory upon recovery from a system crash, but also whether processes can continue their previous execution.…”

Section: Number Of Pwbsmentioning

confidence: 99%

FliT: A Library for Simple and Efficient Persistent Algorithms

Wei,

Ben-David,

Friedman

et al. 2021

Preprint

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Non-volatile random access memory (NVRAM) offers byte-addressable persistence at speeds comparable to DRAM. However, with caches remaining volatile, automatic cache evictions can reorder updates to memory, potentially leaving persistent memory in an inconsistent state upon a system crash. Flush and fence instructions can be used to force ordering among updates, but are expensive. This has motivated significant work studying how to write correct and efficient persistent programs for NVRAM.In this paper, we present FliT, a C++ library that facilitates writing efficient persistent code. Using the library's default mode makes any linearizable data structure durable with minimal changes to the code. FliT avoids many redundant flush instructions by using a novel algorithm to track dirty cache lines. The FliT library also allows for extra optimizations, but achieves good performance even in its default setting.To describe the FliT library's capabilities and guarantees, we define a persistent programming interface, called the P-V Interface, which FliT implements. The P-V Interface captures the expected behavior of code in which some instructions' effects are persisted and some are not. We show that the interface captures the desired semantics of many practical algorithms in the literature.We apply the FliT library to four different persistent data structures, and show that across several workloads, persistence implementations, and data structure sizes, the FliT library always improves operation throughput, by at least 2.1× over a naive implementation in all but one workload.1 While Intel announced the new eADR technology [21] that promises to persist cache contents as well, this would require powerful and expensive batteries to implement. Thus, it is unlikely that volatile caches will cease being a reality in the near future [31].

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“…Persistence is a key property of NVRAMs due to their nonvolatility. Many new persistent data structures have been designed for NVRAMs [7,12,30,31,74,83]. There has also been research on automatic recovery schemes and transactional memory for NVRAMs [3,33,55,59,97,104,108].…”

Section: Related Workmentioning

confidence: 99%

Sage

Dhulipala¹,

McGuffey²,

Kang

et al. 2020

Proc. VLDB Endow.

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Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved memory-density. NVRAM systems today have an order of magnitude more NVRAM than traditional memory (DRAM). NVRAM systems could therefore potentially allow very large graph problems to be solved on a single machine, at a modest cost. However, a significant challenge in achieving high performance is in accounting for the fact that NVRAM writes can be much more expensive than NVRAM reads. In this paper, we propose an approach to parallel graph analytics using the Parallel Semi-Asymmetric Model (PSAM) , in which the graph is stored as a read-only data structure (in NVRAM), and the amount of mutable memory is kept proportional to the number of vertices. Similar to the popular semi-external and semi-streaming models for graph analytics, the PSAM approach assumes that the vertices of the graph fit in a fast read-write memory (DRAM), but the edges do not. In NVRAM systems, our approach eliminates writes to the NVRAM, among other benefits. To experimentally study this new setting, we develop Sage , a parallel semi-asymmetric graph engine with which we implement provably-efficient (and often work-optimal) PSAM algorithms for over a dozen fundamental graph problems. We experimentally study Sage using a 48--core machine on the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) equipped with Optane DC Persistent Memory, and show that Sage outperforms the fastest prior systems designed for NVRAM. Importantly, we also show that Sage nearly matches the fastest prior systems running solely in DRAM, by effectively hiding the costs of repeatedly accessing NVRAM versus DRAM.

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Tracking in Order to Recover - Detectable Recovery of Lock-Free Data Structures

Cited by 11 publications

References 10 publications

Persistent Software Combining

Persistent Software Combining

FliT: A Library for Simple and Efficient Persistent Algorithms

Sage

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