A Generic Parallel Collection Framework

Prokopec, Aleksandar; Bagwell, Phil; Rompf, Tiark; Odersky, Martin

doi:10.1007/978-3-642-23397-5_14

Cited by 52 publications

(37 citation statements)

References 12 publications

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“…Odersky [18] shows how Scala's collections framework, which has the equivalent functional operations coming in Java 8, simplifies the use of parallelism when iterating over Colections. Prokopec et al [22] present and evaluate a framework to build parallel collections (similar with Java 8) and report good speedups.…”

Section: Related Workmentioning

confidence: 99%

Crossing the gap from imperative to functional programming through refactoring

Gyori

Franklin

Dig

et al. 2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

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Java 8 introduces two functional features: lambda expressions and functional operations like map or filter that apply a lambda expression over the elements of a Collection. Refactoring existing code to use these new features enables explicit but unobtrusive parallelism and makes the code more succinct. However, refactoring is tedious: it requires changing many lines of code. It is also error-prone: the programmer must reason about the control-, data-flow, and side-effects. Fortunately, refactorings can be automated.We designed and implemented LambdaFicator, a tool which automates two refactorings. The first refactoring converts anonymous inner classes to lambda expressions. The second refactoring converts for loops that iterate over Collections to functional operations that use lambda expressions. Using 9 open-source projects, we have applied these two refactorings 1263 and 1709 times, respectively. The results show that LambdaFicator is useful: (i) it is widely applicable, (ii) it reduces the code bloat, (iii) it increases programmer productivity, and (iv) it is accurate.

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Section: Related Workmentioning

confidence: 99%

Crossing the gap from imperative to functional programming through refactoring

Gyori

Franklin

Dig

et al. 2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

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“…The iteration execution time is bounded by the size of the linked list that constitutes the main data structure, though this list can dynamically increase by updates interfering the iteration. Another aspect of iterators is addressed in [28] by Prokopec et al, introducing a framework for parallelizing iteration operations in collection data structures. Instead of sequential access to the elements of the collection, they enable parallelization allowing multiple threads to access the elements and thus distributing the workload.…”

Section: Related Work and State Of The Artmentioning

confidence: 99%

A Consistency Framework for Iteration Operations in Concurrent Data Structures

Nikolakopoulos

Gidenstam

Papatriantafilou

et al. 2015

2015 IEEE International Parallel and Distributed Processing Symposium

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Abstract-Concurrent data structures provide the means to multi-threaded applications to share data. Data structures come with a set of predefined operations, specified by the semantics of the data structure. In the literature and in several contemporary commonly used programming environments, the notion of iteration has been introduced for collection data structures, as a bulk operation enhancing the native set of operations. Iterations in several of these contexts have been treated as sequential in nature and may provide weak consistency guarantees when running concurrently with the native operations of the data structures.In this work we study iterations in concurrent data structures in the context of concurrency with the native operations and the guarantees that they provide. Besides linearizability, we propose a set of consistency specifications for such bulk operations, including also concurrency-aware properties by building on Lamport's systematic definitions for registers. Furthermore, by using queues and composite registers as casestudies of underlying objects, we provide a set of constructions of iteration operations, satisfying the properties and showing containment relations. Besides the trade-off between consistency and throughput, we point out and study tradeoff between the overhead of the bulk operation and possible support (helping) by the native operations of the data structure.

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“…One of the aims of Scala Collections [25,26,33,34] is the elimination of code duplication, and one of the mechanisms to achieve this 8 Along with all other sequences we compare against. …”

Section: Arrays As Nodesmentioning

confidence: 99%

“…It also aims to simplify the integration of new data structures with a minimum of effort. In addition, the Scala Parallel Collection API [31][32][33][34] allows parallel collections to integrate seamlessly with the rest of the library. Behind the scenes, Scala Parallel Collections use the Java fork-join pools [18] as a backend for implicit parallelism in the data structure operations.…”

Section: Rrb Treesmentioning

confidence: 99%

RRB vector: a practical general purpose immutable sequence

Stucki

Rompf

Ureche

et al. 2015

Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming

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State-of-the-art immutable collections have wildly differing performance characteristics across their operations, often forcing programmers to choose different collection implementations for each task. Thus, changes to the program can invalidate the choice of collections, making code evolution costly. It would be desirable to have a collection that performs well for a broad range of operations.To this end, we present the RRB-Vector, an immutable sequence collection that offers good performance across a large number of sequential and parallel operations. The underlying innovations are: (1) the Relaxed-Radix-Balanced (RRB) tree structure, which allows efficient structural reorganization, and (2) an optimization that exploits spatio-temporal locality on the RRB data structure in order to offset the cost of traversing the tree.In our benchmarks, the RRB-Vector speedup for parallel operations is lower bounded by 7× when executing on 4 CPUs of 8 cores each. The performance for discrete operations, such as appending on either end, or updating and removing elements, is consistently good and compares favorably to the most important immutable sequence collections in the literature and in use today. The memory footprint of RRB-Vector is on par with arrays and an order of magnitude less than competing collections.

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A Generic Parallel Collection Framework

Cited by 52 publications

References 12 publications

Crossing the gap from imperative to functional programming through refactoring

Crossing the gap from imperative to functional programming through refactoring

A Consistency Framework for Iteration Operations in Concurrent Data Structures

RRB vector: a practical general purpose immutable sequence

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