Talks - Saksham Sharma: A low latency deepdive of Python with Cython

Python’s interpreter (CPython) adds significant overhead by checking types and managing objects for each operation, making simple operations like addition take ~100ns vs 1-10ns in low-level code

Cython can help optimize performance by:

• Allowing static typing of variables and functions
• Converting Python code to C
• Bypassing the Python interpreter for core operations
• Enabling direct memory access through typed memoryviews
• Providing options to release the Global Interpreter Lock (GIL)

Raw untyped Cython code won’t improve performance much - explicit type declarations are needed to get significant speedups

For numerical operations, properly typed Cython code can achieve near-C performance:

• Basic integer operations: ~3-5ns
• Function calls: ~5-10ns
• Array access: ~180ns with typed memoryviews

Real-world considerations:

• I/O operations (disk, network) dwarf interpreter overhead
• Cython is best for optimizing compute-intensive inner loops
• Keep Pythonic APIs for users while optimizing core logic
• Balance between flexibility and performance is key

Performance profiling tools available:

• dis module shows Python bytecode
• Intel PMU provides CPU instruction counts
• Microbenchmarking helps establish baselines

Cython can achieve performance comparable to Rust/C++ when properly optimized, though requires more explicit type declarations and careful tuning

Best practices:

• Cache computations that don’t change
• Use typed memoryviews for array operations
• Explicitly declare types for performance-critical code
• Keep the Python interface clean while optimizing internals