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9. GlossaryΒΆ

ahead-of-time compilation
AOT compilation
AOT
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.
bytecode
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
JIT
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.”
loop-lifting
loop-jitting
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.
lowering
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 @jit decorator 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.
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. Sucessful type inference is a prerequisite for compilation in nopython mode.
typing
The act of running type inference on a value or operation.
ufunc
A NumPy universal function. Numba can create new compiled ufuncs with the @vectorize decorator.