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aseembits93 2026-01-22 17:26:25 -08:00
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docs/support-for-jit/index.mdx
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@ -18,9 +18,8 @@ Each framework uses different compilation strategies to accelerate Python code:
Numba compiles Python functions to optimized machine code using the LLVM compiler infrastructure. Codeflash can suggest Numba optimizations that use:
- **`@jit`** - General-purpose JIT compilation with optional flags.
- **`@jit`** - General-purpose JIT compilation with optional flags. Here is a non-exhaustive options which codeflash would apply on the function to optimize it via numba jit compilation.
- **`noython=True`** - Compiles to machine code without falling back to the python interpreter.
- **`parallel=True`** - Enables automatic thread-level parallelization of the function across multiple CPU cores (no GIL!).
- **`fastmath=True`** - Uses aggressive floating-point optimizations via LLVM's fastmath flag
- **`cache=True`** - Numba writes the result of function compilation to disk which significantly reduces future compilation times.