samaid

import dpnp as np

x = np.asarray([1.0, 2.0, 3.0], device="gpu").astype(np.float32)

y = np.float32(1.0) / x

print(type(x))
print(type(y))
assert isinstance(y, np.dpnp_array.dpnp_array)

samaid

from numba import njit, prange
import numpy as np
from time import time
import math


@njit(parallel=False)
def foo(m):
    pos = np.empty_like(m)
    for i in prange(len(m)):

samaid

from numba import njit, prange
import numpy as np
import math
from time import time

SOFTENING_SQ = 0.001
G = 1.0


@njit(parallel=True)

samaid

from numba.typed import Dict
from numba import types, njit
import numpy as np

# Using Numba dictionary is more efficient for passing into compiled region due to lower unboxing overheads
# The Dict.empty() constructs a typed dictionary.
# The key and value typed must be explicitly declared.
nd = Dict.empty(
    key_type=types.int64,
    value_type=types.float64[:]

samaid

from numba import jitclass
from numba import types
from numba.typed import Dict


@jitclass([('d', types.DictType(types.intp, types.float64))])
class DictWrapper(object):
    def __init__(self):
        d = Dict()
        d[1] = 1.2