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Dynamic property caches: a step towards faster JavaScript proxy objects

Abstract : Inline caches and hidden classes are two essential components for closing the performance gap between static languages such as Java, Scheme, or ML and dynamic languages such as JavaScript or Python. They rely on the observation that for a particular object access located at a particular point of the program, the shapes, usually referred to as the hidden classes, of accessed objects are likely to be the same. Taking benefit of that invariant, they replace the expensive lookup the semantics of these languages normally demand with one test, the inline cache, and a memory read indexed by an offset computed during the last cache miss. These optimizations are essential but they are not general enough to cope with JavaScript’s proxies. In particular, when the property name is itself unknown statically, inline cache-based optimizations always take a slow path. In this paper, we show how to generalize inline caches to cope with an unknown property name. The paper first discusses the general principle of the extension and then presents the experimental results we collected using a modified version of the Hop JavaScript compiler, demonstrating how the optimization is crucial for improving the performance of proxy objects (as they naturally use dynamic property names extensively). The evaluation report shows that the modified Hop outperforms all other implementations of the language, including the most efficient commercial ones, by a factor ranging from 2× to 100×. Even better, our optimizations are applicable to existing compilers as they require only straightforward changes to runtime data structures; no complex analyses are required.
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Contributor : Manuel Serrano <>
Submitted on : Wednesday, December 9, 2020 - 9:09:17 AM
Last modification on : Wednesday, December 16, 2020 - 10:48:45 AM




Manuel Serrano, Robert Bruce Findler. Dynamic property caches: a step towards faster JavaScript proxy objects. CC '20 - 29th International Conference on Compiler Construction, Feb 2020, San Diego CA, United States. pp.108-118, ⟨10.1145/3377555.3377888⟩. ⟨hal-03047893⟩



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