The Inherent Cost of Remembering Consistently

Cohen, Nachshon; Guerraoui, Rachid; Zablotchi, Igor

doi:10.1145/3210377.3210400

Cited by 29 publications

(31 citation statements)

References 43 publications

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“…An alternative approach for achieving recoverability is the design of persistent universal constructions. For example, [7] designed a persistent log-based universal construction that requires only one round trip to NVRAM per operation, which is optimal. However, logging imposes significant overheads in time and space, which are even more pronounced for concurrent data structures, where there is an extra cost of synchronizing accesses to the log.…”

Section: Discussionmentioning

confidence: 99%

Upper and Lower Bounds on the Space Complexity of Detectable Object

Ben-Baruch,

Hendler,

Rusanovsky

2020

Preprint

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The emergence of systems with non-volatile main memory (NVM) increases the interest in the design of recoverable concurrent objects that are robust to crash-failures, since their operations are able to recover from such failures by using state retained in NVM. Of particular interest are recoverable algorithms that, in addition to ensuring object consistency, also provide detectability, a correctness condition requiring that the recovery code can infer if the failed operation was linearized or not and, in the former case, obtain its response.In this work, we investigate the space complexity of detectable algorithms and the external support they require. We make the following three contributions. First, we present the first wait-free bounded-space detectable read/write and CAS object implementations. Second, we prove that the bit complexity of every N -process obstruction-free detectable CAS implementation, assuming values from a domain of size at least N , is Ω(N ). Finally, we prove that the following holds for obstruction-free detectable implementations of a large class of objects: their recoverable operations must be provided with auxiliary state -state that is not required by the non-recoverable counterpart implementation -whose value must be provided from outside the operation, either by the system or by the caller of the operation. In contrast, this external support is, in general, not required if the recoverable algorithm is not detectable.this auxiliary state must be made available to recoverable operations either via their arguments or via a non-volatile variable accessible by them whose value must be modified outside the operation. In contrast, this external support is, in general, not required if the recoverable algorithm is not detectable.The rest of the paper is organized as follows. We describe the system model in Section 2. We then present our bounded-space detectable read/write and CAS algorithms in Sections 3 and 4, respectively. In Section 4 we also prove a lower bound on the space complexity of detectable CAS. This is followed by a proof that detectable implementations of a large class of objects require auxiliary state in Section 5. The paper is concluded by a short discussion in Section 6. System ModelA set P of N asynchronous crash-prone processes communicate through shared objects. The system provides base objects (also called shared variables or registers) that support atomic read, write, and read-modify-write primitive operations. Base objects are used to implement higher-level concurrent objects by defining algorithms, for each process, which use primitive operations to carry out the operations of the implemented object.The state of the system consists of non-volatile shared-memory variables and per-process local variables stored in its local volatile cache. Local variables are accessed only by the process to which they belong. For presentation simplicity, we assume that each process p may own non-volatile private variables that reside in the NVM but are accessed only by p. We also assume...

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Section: Discussionmentioning

confidence: 99%

Upper and Lower Bounds on the Space Complexity of Detectable Object

Ben-Baruch,

Hendler,

Rusanovsky

2020

Preprint

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“…The wait-free version shares some ideas with PSim, thus integrating some form of combining, but it inherits the helping and logging mechanisms from the lock-free version. ONLL [10] is a log-based persistent universal construction which ensures durable linearizability [24] and lockfreedom. ONLL performs just one persistent fence for each update operation invoked and avoids performing persistence fences for read operations.…”

Section: Related Workmentioning

confidence: 99%

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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“…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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The Inherent Cost of Remembering Consistently

Cited by 29 publications

References 43 publications

Upper and Lower Bounds on the Space Complexity of Detectable Object

Upper and Lower Bounds on the Space Complexity of Detectable Object

Persistent Software Combining

Sage

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