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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
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Specializing generic Java data structures

Authors: Dan Graur; Rodrigo Bruno; Gustavo Alonso;

Specializing generic Java data structures

Abstract

The Collections framework is an essential utility in virtually every Java application. It offers a set of fundamental data structures that exploit Java Generics and the Object type in order to enable a high degree of reusability. Upon instantiation, Collections are parametrized by the type they are meant to store. However, at compile-time, due to type erasure, this type gets replaced by Object, forcing the data structures to manipulate references of type Object (the root of the Java type system). In the bytecode, the compiler transparently adds type checking instructions to ensure type safety, and generates bridge methods to enable the polymorphic behavior of parametrized classes. This approach can introduce non-trivial runtime overheads when applications extensively manipulate Collections. We propose the Java Collections Specializer (JCS), a tool we have developed to deliver truly specialized Collections. JCS can generate ArrayLists, ConcurrentHashMaps and HashMaps with true type specialization that incur no performance penalties due to bridge methods or type checking instructions. JCS offers the possibility to easily extend its use to other Collection data structures. Since the specialized data structures extend and inherit from the generic counterpart's superclasses and interfaces, the specialized versions can be used in most places where generic versions are employed. The programmer uses JCS to generate specializations ahead of time. These are generated under the java.util package, and need only be added to the class path and integrated into the application logic. We show that the specialized data structures can improve the runtime performance of data intensive workloads by up to 14% for read use-cases and 42% for write use-cases.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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