google/gematria
Machine learning for machine code.
This framework helps software engineers and compiler developers analyze and optimize the performance of machine code. It takes raw machine code or basic blocks as input and uses machine learning models to estimate their inverse throughput, helping identify performance bottlenecks. The primary users are those involved in compiler design, low-level optimization, or performance engineering for various CPU architectures.
Use this if you are a compiler developer or performance engineer looking to apply machine learning to predict the execution speed of machine code to guide optimization efforts.
Not ideal if you are looking for a general-purpose machine learning library or a tool to profile high-level application performance.
Stars
95
Forks
18
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 13, 2026
Commits (30d)
0
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