From Python Slop to 4µs Rust: How We Accelerated Market Microstructure Simulations by 25,000x
📰 Medium · Data Science
Accelerate market microstructure simulations by 25,000x by migrating from Python to Rust, learning how to optimize performance-critical code
Action Steps
- Identify performance-critical code in Python using profiling tools
- Migrate performance-critical code to Rust for optimization
- Use Rust's concurrency features to parallelize simulations
- Apply benchmarking and testing to ensure correctness and measure performance gains
- Integrate Rust code with existing Python ecosystem using foreign function interfaces (FFI)
Who Needs to Know This
Quantitative ML teams and data scientists can benefit from this approach to improve simulation performance, leading to faster training and validation cycles
Key Insight
💡 Rust can provide significant performance improvements over Python for performance-critical code, making it a viable option for quantitative ML teams
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💡 Accelerate market microstructure simulations by 25,000x with Rust! Learn how to migrate performance-critical code from Python to Rust for massive performance gains
Key Takeaways
Accelerate market microstructure simulations by 25,000x by migrating from Python to Rust, learning how to optimize performance-critical code
Full Article
Every quantitative ML team knows the drill: your training ecosystem is locked into Python. From feature engineering to validation, Python… Continue reading on Medium »
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