Experimental Features#

Numba-dpex includes various experimental features that are not yet suitable for everyday production usage, but are included as an engineering preview. The most prominent experimental features currently included in numba-dpex are listed in this section.

Compiling and Offloading dpnp statements#

Data Parallel Extension for NumPy* (dpnp) is a drop-in NumPy* replacement library built using the oneAPI software stack including oneMKL, oneDPL and SYCL*. numba-dpex has experimental support for compiling a subset of dpnp functions. The feature is enabled by the numba_dpex.dpjit() decorator.

An example of a supported usage of dpnp in numba-dpex is provided in the following code snippet:

import dpnp
from numba_dpex import dpjit


@dpjit
def foo():
    a = dpnp.ones(1024, device="gpu")
    return dpnp.sqrt(a)


a = foo()
print(a)
print(type(a))

Offloading prange loops#

numba-dpex supports using the numba.prange statements with dpnp.ndarray objects. All such prange loops are offloaded as kernels and executed on a device inferred using the compute follows data programming model. The next examples shows using a prange loop.

import dpnp
from numba_dpex import dpjit, prange


@dpjit
def foo():
    x = dpnp.ones(1024, device="gpu")
    o = dpnp.empty_like(a)
    for i in prange(x.shape[0]):
        o[i] = x[i] * x[i]
    return o


c = foo()
print(c)
print(type(c))

prange loop statements can also be used to write reduction loops as demonstrated by the following naive pairwise distance computation.

from numba_dpex import dpjit, prange
import dpnp
import dpctl


@dpjit
def pairwise_distance(X1, X2, D):
    """Naïve pairwise distance impl - take an array representing M points in N
    dimensions, and return the M x M matrix of Euclidean distances

    Args:
        X1 : Set of points
        X2 : Set of points
        D  : Outputted distance matrix
    """
    # Size of inputs
    X1_rows = X1.shape[0]
    X2_rows = X2.shape[0]
    X1_cols = X1.shape[1]

    float0 = X1.dtype.type(0.0)

    # Outermost parallel loop over the matrix X1
    for i in prange(X1_rows):
        # Loop over the matrix X2
        for j in range(X2_rows):
            d = float0
            # Compute exclidean distance
            for k in range(X1_cols):
                tmp = X1[i, k] - X2[j, k]
                d += tmp * tmp
            # Write computed distance to distance matrix
            D[i, j] = dpnp.sqrt(d)


q = dpctl.SyclQueue()
X1 = dpnp.ones((10, 2), sycl_queue=q)
X2 = dpnp.zeros((10, 2), sycl_queue=q)
D = dpnp.empty((10, 2), sycl_queue=q)

pairwise_distance(X1, X2, D)
print(D)

Kernel fusion#