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Profile Guided Offline Optimization of Hidden Class Graphs for JavaScript VMs in Embedded

Talkat VMIL 2022 · on Monday, 2022-12-05· [javascript, web-performance]
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slides:none on file

Collects hidden‑class graphs from a profiling run, rewrites the graph offline to remove intermediate transition nodes and to move unlikely property branches to the end of allocation order, then ships the optimized graph to the production VM. Implementation in eJSVM applies in‑object property allocation to avoid indirection, reduces polymorphism of inline caches, and reports 61.9% fewer hidden classes on average with an overall ~17% object memory footprint reduction and mixed runtime speed changes.

AI-written summary · about

  1. 0:00Start
  2. 4:00JavaScript Object
  3. 6:45Hidden Class Transition
  4. 7:38Hidden Class Graph
  5. 22:28Run-time optimisation
  6. 23:19in-object allocation
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