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Benchmarks for argument dispatching and call overhead of ``@jit`` functions.
import numpy as np
rec_dtype = np.dtype([('a', np.float64),
('b', np.int32),
('c', np.complex64),
samples = {
'bool': True,
'int': 100000,
'float': 0.5,
'complex': 0.5 + 1.0j,
'array_1d': np.zeros(10, dtype=np.int64),
'array_3d': np.zeros(20, dtype=np.float64).reshape(2, 2, 5),
'array_records': np.zeros(10, dtype=rec_dtype),
'recarray': np.recarray(10, dtype=rec_dtype),
'tuple': (0.5, 1.0j, ()),
'record': np.empty(1, dtype=rec_dtype)[0],
'bytearray': bytearray(3),
def setup():
Precompile jitted functions. This will register many specializations
to choose from.
from numba import jit
global binary, binary_pyobj, unary_default
def binary(x, y):
def binary_pyobj(x, y):
def unary_default(x=None):
for tp in samples.values():
binary(tp, tp)
binary_pyobj(object(), object())
class NoPythonDispatch:
Time dispatching to a jitted function's specializations based on argument
This stresses two things:
- the typing of arguments (from argument value to typecode)
- the selection of the best specialization amongst all the known ones
# We repeat 1000 times so as to make the overhead of benchmark launching
# negligible.
def generate_benchmarks(cls, names):
for name in names:
def timefunc(self, arg=samples[name]):
func = binary
for i in range(1000):
func(arg, arg)
timefunc.__name__ = "time_dispatch_" + name
setattr(cls, timefunc.__name__, timefunc)
def time_dispatch_defaults(self):
class PyObjectDispatch:
def time_dispatch_pyobject(self):
x = object()
for i in range(1000):
binary_pyobj(x, x)
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