# See the License for the specific language governing permissions and
# limitations under the License.
import dpctl
import syclbuffer_naive as sb
import numpy as np
X = np.full((10 ** 4, 4098), 1e-4, dtype="d")
# warm-up
print("=" * 10 + " Executing warm-up " + "=" * 10)
print("NumPy result: ", X.sum(axis=0))
print(
"SYCL(default_device) result: {}".format(
sb.columnwise_total(X),
)
)
import timeit
print(
"Running time of 100 calls to columnwise_total on matrix with shape {}".format(
X.shape
)
)
print("Times for default_selector, inclusive of queue creation:")
print(
timeit.repeat(
stmt="sb.columnwise_total(X)",
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import timeit
import numpy as np
import syclbuffer_naive as sb
X = np.full((10**4, 4098), 1e-4, dtype="d")
# warm-up
print("=" * 10 + " Executing warm-up " + "=" * 10)
print("NumPy result: ", X.sum(axis=0))
print("SYCL(default_device) result: {}".format(sb.columnwise_total(X), ))
print("Running time of 100 calls to columnwise_total on matrix with "
"shape {}".format(X.shape))
print("Times for default_selector, inclusive of queue creation:")
print(
timeit.repeat(
stmt="sb.columnwise_total(X)",
setup="sb.columnwise_total(X)", # ensure JIT compilation is not counted
number=100,
globals=globals(),
))
print("Times for NumPy")
print(timeit.repeat(stmt="X.sum(axis=0)", number=100, globals=globals()))
import syclbuffer_naive as sb import numpy as np X = np.random.randn(20, 10) # compute column-wise total with NumPy's own host code print(X.sum(axis=0)) # compute column-wise total with SYCL extension print(sb.columnwise_total(X))