NBmalloc: Allocating Memory in a Lock-Free Manner

Gidenstam, Anders; Papatriantafilou, Marina; Tsigas, Philippas

doi:10.1007/s00453-008-9268-x

Cited by 19 publications

(11 citation statements)

References 32 publications

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“…Providing the appropriate data structures that best fit the needs of a concurrent application is a key research issue, as emphasized by [25,19] and also seen in examples such as [8,30]. In this paper, we study data structures as articulation points in the context of streaming aggregation, analysing the concurrency needs and proposing methods to meet them.…”

Section: Discussionmentioning

confidence: 99%

Efficient Data Streaming Multiway Aggregation through Concurrent Algorithmic Designs and New Abstract Data Types

Gulisano

Nikolakopoulos

Cederman

et al. 2017

ACM Trans. Parallel Comput.

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Data streaming relies on continuous queries to process unbounded streams of data in a real-time fashion. It is commonly demanding in computation capacity, given that the relevant applications involve very large volumes of data. Data structures act as articulation points and maintain the state of data streaming operators, potentially supporting high parallelism and balancing the work between them. Prompted by this fact, in this work we study and analyze parallelization needs of these articulation points, focusing on the problem of streaming multiway aggregation, where large data volumes are received from multiple input streams. The analysis of the parallelization needs, as well as of the use and limitations of existing aggregate designs and their data structures, leads us to identify needs for proper shared objects that can achieve low-latency and high-throughput multiway aggregation. We present the requirements of such objects as abstract data types and we provide efficient lock-free linearizable algorithmic implementations of them, along with new multiway aggregate algorithmic designs that leverage them, supporting both deterministic order-sensitive and order-insensitive aggregate functions. Furthermore, we point out future directions that open through these contributions. The paper includes an extensive experimental study, based on a variety of aggregation continuous queries on two large datasets extracted from SoundCloud, a music social network, and from a Smart Grid network. In all the experiments, the proposed data structures and the enhanced aggregate operators improved the processing performance significantly, up to one order of magnitude, in terms of both throughput and latency, over the commonly-used techniques based on queues.

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Section: Discussionmentioning

confidence: 99%

Efficient Data Streaming Multiway Aggregation through Concurrent Algorithmic Designs and New Abstract Data Types

Gulisano

Nikolakopoulos

Cederman

et al. 2017

ACM Trans. Parallel Comput.

Self Cite

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“…They used memory efficiently but were highly serial, constituting an obstacle to the throughput of concurrent applications, which require some form of synchronization to protect the heap. Additionally, when a concurrent application is run in a multiprocessor system, other problems can occur, such heap blowup, false sharing, or memory contention (Masmano et al 2006;Gidenstam et al 2010).…”

Section: Fixed-size Memory Allocatormentioning

confidence: 99%

Table space designs for implicit and explicit concurrent tabled evaluation

Areias¹,

Rocha²

2018

Theory and Practice of Logic Programming

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One of the main advantages of Prolog is its potential for the implicit exploitation of parallelism and, as a high-level language, Prolog is also often used as a means to explicitly control concurrent tasks. Tabling is a powerful implementation technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant subcomputations. Given these advantages, the question that arises is if tabling has also the potential for the exploitation of concurrency/parallelism. On one hand, tabling still exploits a search space as traditional Prolog but, on the other hand, the concurrent model of tabling is necessarily far more complex, since it also introduces concurrency on the access to the tables. In this paper, we summarize Yap's main contributions to concurrent tabled evaluation and we describe the design and implementation challenges of several alternative table space designs for implicit and explicit concurrent tabled evaluation that represent different tradeoffs between concurrency and memory usage. We also motivate for the advantages of using fixed-size and lock-free data structures, elaborate on the key role that the engine's memory allocator plays on such environments, and discuss how Yap's mode-directed tabling support can be extended to concurrent evaluation. Finally, we present our future perspectives toward an efficient and novel concurrent framework which integrates both implicit and explicit concurrent tabled evaluation in a single Prolog engine.

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“…Composing lock-free concurrent data objects, in the context that we consider in this paper, has been an open problem in the area of lock-free data objects. There exists customized compositions of specific concurrent data objects, including the composition of lock-free flat-sets by Gidenstam et al that constitute the foundation of a lock-free memory allocator [9,8], but no generic solution.…”

Section: Composingmentioning

confidence: 99%

Supporting lock-free composition of concurrent data objects

CedermanDaniel¹,

Tsigas²

2010

SIGPLAN Not.

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Lock-free data objects offer several advantages over their blocking counterparts, such as being immune to deadlocks and convoying and, more importantly, being highly concurrent. However, composing the operations they provide into larger atomic operations, while still guaranteeing efficiency and lock-freedom, is a challenging algorithmic task.We present a lock-free methodology for composing highly concurrent linearizable objects together by unifying their linearization points. This makes it possible to relatively easily introduce atomic lock-free move operations to a wide range of concurrent objects. Experimental evaluation has shown that the operations originally supported by the data objects keep their performance behavior under our methodology.

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NBmalloc: Allocating Memory in a Lock-Free Manner

Cited by 19 publications

References 32 publications

Efficient Data Streaming Multiway Aggregation through Concurrent Algorithmic Designs and New Abstract Data Types

Efficient Data Streaming Multiway Aggregation through Concurrent Algorithmic Designs and New Abstract Data Types

Table space designs for implicit and explicit concurrent tabled evaluation

Supporting lock-free composition of concurrent data objects

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