Source code for hpctestlib.python.numpy.numpy_ops

# Copyright 2016-2022 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
# SPDX-License-Identifier: BSD-3-Clause

import reframe as rfm
import reframe.utility.sanity as sn

[docs]@rfm.simple_test class numpy_ops_check(rfm.RunOnlyRegressionTest, pin_prefix=True): '''NumPy basic operations test. `NumPy <>`__ is the fundamental package for scientific computing in Python. It provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more. This test test performs some fundamental NumPy linear algebra operations (matrix product, SVD, Cholesky decomposition, eigendecomposition, and inverse matrix calculation) and users the execution time as a performance metric. The default assumption is that NumPy is already installed on the currest system. ''' executable = 'python' executable_opts = [''] descr = 'Test NumPy operations: dot, svd, cholesky, eigen and inv'
[docs] @performance_function('s', perf_key='dot') def time_dot(self): '''Time of the ``dot`` kernel in seconds.''' return sn.extractsingle( r'^Dotted two 4096x4096 matrices in\s+(?P<dot>\S+)\s+s', self.stdout, 'dot', float)
[docs] @performance_function('s', perf_key='svd') def time_svd(self): '''Time of the ``svd`` kernel in seconds.''' return sn.extractsingle( r'^SVD of a 2048x1024 matrix in\s+(?P<svd>\S+)\s+s', self.stdout, 'svd', float)
[docs] @performance_function('s', perf_key='cholesky') def time_cholesky(self): '''Time of the ``cholesky`` kernel in seconds.''' return sn.extractsingle( r'^Cholesky decomposition of a 2048x2048 matrix in' r'\s+(?P<cholesky>\S+)\s+s', self.stdout, 'cholesky', float)
[docs] @performance_function('s', perf_key='eigendec') def time_eigendec(self): '''Time of the ``eigendec`` kernel in seconds.''' return sn.extractsingle( r'^Eigendecomposition of a 2048x2048 matrix in' r'\s+(?P<eigendec>\S+)\s+s', self.stdout, 'eigendec', float)
[docs] @performance_function('s', perf_key='inv') def time_inv(self): '''Time of the ``inv`` kernel in seconds.''' return sn.extractsingle( r'^Inversion of a 2048x2048 matrix in\s+(?P<inv>\S+)\s+s', self.stdout, 'inv', float)
@sanity_function def assert_numpy_version(self): return sn.assert_found(r'Numpy version:\s+\S+', self.stdout)