Extending Basic Block Versioning with Typed Object Shapes
July 09, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Maxime Chevalier-Boisvert, Marc Feeley
arXiv ID
1507.02437
Category
cs.PL: Programming Languages
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Typical JavaScript (JS) programs feature a large number of object property accesses. Hence, fast property reads and writes are crucial for good performance. Unfortunately, many (often redundant) dynamic checks are implied in each property access and the semantic complexity of JS makes it difficult to optimize away these tests through program analysis. We introduce two techniques to effectively eliminate a large proportion of dynamic checks related to object property accesses. Typed shapes enable code specialization based on object property types without potentially complex and expensive analyses. Shape propagation allows the elimination of redundant shape checks in inline caches. These two techniques combine particularly well with Basic Block Versioning (BBV), but should be easily adaptable to tracing Just-In-Time (JIT) compilers and method JITs with type feedback. To assess the effectiveness of the techniques presented, we have implemented them in Higgs, a type-specializing JIT compiler for JS. The techniques are compared to a baseline using polymorphic Inline Caches (PICs), as well as commercial JS implementations. Empirical results show that across the 26 benchmarks tested, these techniques eliminate on average 48% of type tests, reduce code size by 17% and reduce execution time by 25%. On several benchmarks, Higgs performs better than current production JS virtual machines
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted