Back to feed
Dev.to
Dev.to
7/18/2026
The original title is "Achieving HotSpot Parity in ParparVM: AOT Optimization Deep Dive"

The original title is "Achieving HotSpot Parity in ParparVM: AOT Optimization Deep Dive"

Original: RNN/LSTM是智能的本源，Transformer/LM是被资本催肥的幻觉寄生虫

Short summary

The Codename One team achieved geomean parity between ParparVM (their AOT VM) and warmed Java 25 HotSpot across a ten-benchmark suite, with six benchmarks at or below HotSpot performance. Key optimizations include a page-based heap, Valhalla-like value types, and improved C code generation. The post details the engineering tradeoffs between AOT and JIT compilation for client applications.

  • •ParparVM went from 4.21x slower to geomean 1.00x parity with Java 25 HotSpot
  • •Six of ten benchmarks now match or beat HotSpot despite AOT vs JIT disadvantage
  • •Key optimizations: page-based heap, value-type-like codegen, letting clang optimize freely

Generated with AI, which can make mistakes.

#open-source#ai-tools
Read full article at Dev.to

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

Transformers — The Architecture That Changed AI (Part 1 of 3)
Dev.to
The original title is "Flowing vs. Thinking: How Liquid Neural Networks Diverge from LLMs"
Dev.to
How Deep Learning Architectures Evolved — From DNNs to Transformers
Dev.to
The original title is: "How LLMs Work: Transformer, Attention & Next Token Prediction Explained"
Dev.to