Type feedback vs. concrete type inference

Author:

Agesen Ole1,Hölzle Urs2

Affiliation:

1. Computer Science Department, Stanford University, Stanford, CA

2. Computer Science Department, University of California, Santa Barbara, CA

Abstract

Two promising optimization techniques for object-oriented languages are type feedback (profile-based receiver class prediction) and concrete type inference (static analysis). We directly compare the two techniques, evaluating their effectiveness on a suite of 23 S ELF programs while keeping other factors constant.Our results show that both systems inline over 95% of all sends and deliver similar overall performance with one exception: S ELF 's automatic coercion of machine integers to arbitrary-precision integers upon overflow confounds type inference and slows down arithmetic-intensive benchmarks.We discuss several other issues which, given the comparable run-time performance, may influence the choice between type feedback and type inference.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference29 articles.

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Static Type Recommendation for Python;Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering;2022-10-10

2. Improved type specialization for dynamic scripting languages;ACM SIGPLAN Notices;2014-02-05

3. Improved type specialization for dynamic scripting languages;Proceedings of the 9th symposium on Dynamic languages - DLS '13;2013

4. Fast and precise hybrid type inference for JavaScript;ACM SIGPLAN Notices;2012-08-06

5. The impact of optional type information on jit compilation of dynamically typed languages;ACM SIGPLAN Notices;2012-03-18

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