solutions.append(values_one_step)
return solutions
def bench(n_sleds, n_time):
x_init = np.zeros(n_sleds)
y_init = np.random.rand(n_sleds)
v_init = np.zeros(n_sleds)
u_init = np.zeros(n_sleds) + 3.5
solver(board, x_init, y_init, u_init, v_init, 0.01, n_time)
# end code functions (don't remove this line)
bench_pythran = jit(bench)
# Numba does not support this code...
# bench_numba = jit(backend="numba")(bench)
from transonic import wait_for_all_extensions
# warmup (compilation of the Pythran extension)
bench_pythran(1, 1)
wait_for_all_extensions()
if __name__ == "__main__":
from transonic.util import timeit_verbose as timeit
n_sleds = 10
n_time = 200
import numpy as np
from scipy.integrate import odeint
from transonic.util import timeit_verbose as timeit
import numba
from transonic import jit, wait_for_all_extensions
def lorenz(u, t):
x, y, z = u
return 10.0 * (y - x), x * (28.0 - z) - y, x * y - 2.66 * z
lorenz_pythran = jit(native=True, xsimd=True)(lorenz)
lorenz_numba = numba.jit(lorenz)
u0 = (1.0, 0.0, 0.0)
lorenz_pythran(u0, 0)
lorenz_numba(u0, 0)
wait_for_all_extensions()
# tspan = (0.0, 100.0)
t = np.linspace(0, 100, 1001)
sol = odeint(lorenz, u0, t)
norm = timeit("odeint(lorenz, u0, t, rtol = 1e-8, atol=1e-8)", globals=locals())
timeit(
"odeint(lorenz_pythran, u0, t, rtol = 1e-8, atol=1e-8)",
globals=locals(),
return solutions
def bench():
n_sleds = 10
n_time = 1000
x_init = np.zeros(n_sleds)
y_init = np.random.rand(n_sleds)
v_init = np.zeros(n_sleds)
u_init = np.zeros(n_sleds) + 3.5
solver(board, x_init, y_init, v_init, u_init, 0.01, n_time, n_sleds)
bench_pythran = jit(bench)
bench_numba = jit(backend="numba")(bench)
if __name__ == "__main__":
from transonic.util import timeit_verbose as timeit
g = locals()
norm = timeit("bench()", globals=g)
timeit("bench_pythran()", globals=g, norm=norm)
timeit("bench_numba()", globals=g, norm=norm)
"""
bench : 1.000 * norm
norm = 8.35e-01 s
bench_pythran : 0.007 * norm
bench_numba : 0.009 * norm
from transonic import jit # from .base import identity # FIXME: The above import works but creating an alias does not from .base import identity as base_identity identity_jit = jit(native=True)(base_identity)
pisum_vec,
parse_int,
parse_int_rand,
parse_int_randomgen,
printfd,
# abs2,
# mandel,
mandelperf,
mandelperf2,
broadcast,
broadcast_inplace,
)
args_jit = dict(native=True, xsimd=True)
fib = jit(fib, **args_jit)
qsort_kernel = jit(qsort_kernel, **args_jit)
matrix_statistics_rand = jit(matrix_statistics_rand, **args_jit)
matrix_statistics_ones = jit(matrix_statistics_ones, **args_jit)
matrix_multiply_rand = jit(matrix_multiply_rand, **args_jit)
matrix_multiply_ones = jit(matrix_multiply_ones, **args_jit)
broadcast = jit(broadcast, **args_jit)
broadcast_inplace = jit(broadcast_inplace, **args_jit)
bench_random = jit(bench_random, **args_jit)
pisum = jit(pisum, **args_jit)
pisum_vec = jit(pisum_vec, **args_jit)
import numpy as np
from transonic import jit, boost
from transonic.mpi import Path
def func0(a):
return 2 * a
def func():
return 1
func0_jitted = jit(func0)
@jit
def func1(a: "int[][] or float[]", l: "int list"):
tmp = np.exp(sum(l))
result = tmp * a * func0(a) + func()
return result
@jit
def func_identity(a):
return a
# weird but done on purpose for a better coverage
Path(__file__).touch()
from transonic import jit
import numba
from pure_numpy import laplace_numpy, laplace_loops
laplace_transonic_pythran = jit(native=True, xsimd=True)(laplace_numpy)
laplace_transonic_python = jit(backend="python")(laplace_numpy)
laplace_transonic_numba = jit(backend="numba")(laplace_numpy)
laplace_numba = numba.njit(laplace_numpy)
laplace_transonic_pythran_loops = jit(native=True, xsimd=True)(laplace_loops)
laplace_transonic_python_loops = jit(backend="python")(laplace_loops)
laplace_transonic_numba_loops = jit(backend="numba")(laplace_loops)
laplace_numba_loops = numba.njit(laplace_loops)
if __name__ == "__main__":
from transonic import wait_for_all_extensions
from skimage.data import astronaut
from skimage.color import rgb2gray
image = astronaut()
image = rgb2gray(image)
# warm the functions
laplace_transonic_python(image)
laplace_transonic_pythran(image)
laplace_transonic_pythran_loops(image)
laplace_transonic_numba(image)
laplace_transonic_numba_loops(image)
laplace_numba(image)
from scipy.integrate import quad
from scipy import LowLevelCallable
import numba
from transonic.util import timeit_verbose as timeit
from transonic import jit, boost, wait_for_all_extensions
from util import integrand as integrand_capsule
def integrand(x):
return np.exp(10.0 * x * np.cos(x))
integrand_transonic_jit = jit(native=True)(integrand)
integrand_transonic_jit(0.0)
wait_for_all_extensions()
# transonic bug (should work)!
# integrand_transonic_boost = boost(integrand)
@boost
def integrand_transonic_boost(x: float):
return np.exp(10.0 * x * np.cos(x))
integrand_numba = numba.njit(integrand)
integrand_numba_cfunc = numba.cfunc("float64(float64)")(integrand)
from transonic import jit
def func(x):
return x**2
def func2(x):
return x**2
func_jitted = jit(func)
