- ahead-of-time compilation
- AOT compilation
Compilation of a function in a separate step before running the program code, producing an on-disk binary object which can be distributed independently. This is the traditional kind of compilation known in languages such as C, C++ or Fortran.
- Python bytecode
The original form in which Python functions are executed. Python bytecode describes a stack-machine executing abstract (untyped) operations using operands from both the function stack and the execution environment (e.g. global variables).
- compile-time constant
An expression whose value Numba can infer and freeze at compile-time. Global variables and closure variables are compile-time constants.
- just-in-time compilation
- JIT compilation
Compilation of a function at execution time, as opposed to ahead-of-time compilation.
- JIT function
Shorthand for “a function JIT-compiled with Numba using the @jit decorator.”
- lifted loops
A feature of compilation in object mode where a loop can be automatically extracted and compiled in nopython mode. This allows functions with operations unsupported in nopython mode to see significant performance improvements if they contain loops with only nopython-supported operations.
The act of translating Numba IR into LLVM IR. The term “lowering” stems from the fact that LLVM IR is low-level and machine-specific while Numba IR is high-level and abstract.
- nopython mode
A Numba compilation mode that generates code that does not access the Python C API. This compilation mode produces the highest performance code, but requires that the native types of all values in the function can be inferred. Unless otherwise instructed, the
@jitdecorator will automatically fall back to object mode if nopython mode cannot be used.
- Numba IR
- Numba intermediate representation
A representation of a piece of Python code which is more amenable to analysis and transformations than the original Python bytecode.
- object mode
A Numba compilation mode that generates code that handles all values as Python objects and uses the Python C API to perform all operations on those objects. Code compiled in object mode will often run no faster than Python interpreted code, unless the Numba compiler can take advantage of loop-jitting.
OptionalTypeis effectively a type union of a
None. They typically occur in practice due to a variable being set to
Noneand then in a branch the variable being set to some other value. It’s often not possible at compile time to determine if the branch will execute so to permit type inference to complete, the type of the variable becomes the union of a
type(from the value) and
- type inference
The process by which Numba determines the specialized types of all values within a function being compiled. Type inference can fail if arguments or globals have Python types unknown to Numba, or if functions are used that are not recognized by Numba. Successful type inference is a prerequisite for compilation in nopython mode.
The act of running type inference on a value or operation.
A NumPy universal function. Numba can create new compiled ufuncs with the @vectorize decorator.
In numba, when a mutable container is passed as argument to a nopython function from the Python interpreter, the container object and all its contained elements are converted into nopython values. To match the semantics of Python, any mutation on the container inside the nopython function must be visible in the Python interpreter. To do so, Numba must update the container and its elements and convert them back into Python objects during the transition back into the interpreter.
Not to be confused with Python’s “reflection” in the context of binary operators (see https://docs.python.org/3.5/reference/datamodel.html).